Development of advanced sensor materials and encryption techniques for secure Wireless Body Area Networks (WBANs) in healthcare applications

ABSTRACT

Advancements in the fundamental healthcare technology known as Wireless Body Area Networks transform wearables into a medium to monitor healthcare in real time. Advanced materials become essential for sensor fabrication within body-deployed systems that need to collect precise data and maintain low power requirements and extended lifecycles. Security issues persist in integrating these sensors into secure communication networks when health data needs protection. This paper investigates sensor materials used in WBANs, emphasizing their performance aspects such as energy efficiency, data protection, and security measures. A new security framework adopts both Merkle-Hellman encryption and transmission techniques that use compression to defend WBANs data transmission. Our approach brings together advanced sensor materials and lightweight encryption algorithms to improve WBAN reliability while ensuring security at an optimal performance level. This research presents a new way to enhance the security and performance of Wireless Body Area Networks (WBANs) by using improved sensor materials along with Merkle-Hellman encryption and compression methods. This new framework enhances energy economy, data security, and net-work resilience, making it a promising choice for future healthcare applications. Experimental tests prove that combining such materials with cryptographic methods enhances network efficiency and data security and energy management to offer a promising solution for healthcare applications of the future.

Keywords:
Wireless Body Area Networks; Sensor materials; Healthcare; Merkle-Hellman algorithm; Security protocols

1. INTRODUCTION

WBANs are based on Radio Frequency (RF) technology to connect small nodes with sensor or actuator functions based on wireless networking technology. They also integrate a system that collects data from sensors using WBAN technology to monitor vital signs obtained through the dataset. Similarly, a small wireless network consists of many small sensors and actuators. In addition, they use sensor nodes placed above or below the skin to capture a variety of data, including CD, Electroencephalogram (EEG), body movement, and respiratory rate [¹]. The sensors were designed considering the specific needs of the end user. Using WBAN technology, patients can easily access medical records in the dataset to avoid unnecessary hospital visits and receive treatment.

WBAN gathers medical data through wireless internet and displays it on the screen using cat litter sensors. The sensor may or may not be connected to the main body, depending on its use. This sensor can be used to gather vital information about athletes and sportspeople in the medical and sports domains [⁶]. The WBAN is connected to the Internet technologies, which consist of cellular systems, Bluetooth, Wi-Fi, ZigBee, and Wireless Personal Area Network (WPAN). Sensors collect patient data, which is then transmitted to the cloud via various technologies for medical specialists to access. Wearable and implantable nodes are the two primary node types in WBANs, and they operate on separate frequencies. The 400 MHz Medical Implanted Communication Service (MICS) can be used on implantable nodes [², ³].

WBAN systems and their infrastructure are globally distributed in comparison to a normal healthcare system and have greater performance, data integrity and data security issues [⁴²]. The technological barriers to “making it work” should be eliminated. However, to guarantee patient safety, other concerns must also be resolved. Human factors, system security, safety, accuracy, repeatability, fault tolerance, and reliability mostly cause these failures. To guarantee patient and data security, secure connections must be made and kept between (a) confirming the wearer’s identification, (b) safeguarding the wearer’s privacy, and (c) the wearer’s data [⁸]. The access to data and integrity of sensor data until the time when it is permanently stored are ensured by individual sensors and their parent devices (d). Thus, the data has to be encrypted before transmitting it through a wireless network.

Thousands of people are influenced by these securities. Also, that the distances covered by WBAN settings are short is one advantage associated with the settings. EEG refers to a device that measures the brain activity. Another example is an ECG sensor, which is designed to monitor the cardiac activity. WBAN nodes are mandatory to the network and are incorporated into the body [⁵]. There are more subtypes of nodes depending on their implementation.

“Implant node” describes a node that is inserted either intramuscularly or subcutaneously. Based on how they perform inside the network, nodes in a WBAN are categorized into one of three kinds. Establish a connection with a trust centre, access coordinator, another WBAN, or the outside world. The coordinator coordinates the WBAN, a Personal Digital Assistant (PDA), enabling communication between all nodes. “End nodes” are nodes whose only function is to finish the intended application; they are unable to pass messages to other nodes. These nodes are called relay nodes and serve as intermediaries. A relay node is made up of parent and child nodes that work together to carry data. Before the data reaches the PDA, it must be relayed by other nodes from the ground node. The WBAN sensors control a movement device that has an internal reservoir, such that the patients are provided with the right dosage of hypoglycemic medication. Other applications are education, entertainment, human-computer interaction, medical and physiological monitoring and environmental monitoring [⁶, ⁷].

Users tend to encrypt files and then upload them on the cloud server when sharing data via WBAN. Nonetheless, the traditional symmetric and public-key encryption strategies cannot cope with the high-level communication networks such as WBAN. The patient must just significantly recognize the traits of the person who gains access to the shared data [⁴⁴]. Attribute-Based Encryption (ABE) involves an attribute authority assigning attributes to each user and generating a different key of encryption privatized per user [⁹]. The process of decryption uses a private key and user attributes that are corresponding to the access hierarchy of the data owner. Consequently, the ABE methodology is thought to be the most simple and effective access control mechanism for encrypting data in the cloud storage environment’s WBAN. The primary objective of this endeavor is to develop a framework for reliably and securely transmitting data packets gathered by wireless body sensor networks. This work presents a method that prioritizes data packet compression while maintaining reconstruction quality. To lower data quality, this paper suggested spatial data compression approaches. An iterative thresholding technique guarantees high-quality outcomes when reconstructing data. Hierarchical clustering classifies all nodes in the environment to ensure optimal routing. In this study, the network is protected from malicious users by using Merkle-Hellman knapsack technology following data compression.

This research consists of five main sections. Section 1 provides an introduction and summary of the paper. Section 2 discusses how different researchers address the problem of confidentiality and privacy in their works with the methods discussed. Part III explores the application of WBANs in e-health, encryption techniques, security requirements, and potential risks. It further discusses hierarchical node selection and optimum routing with respect to route selection. The last part points out the advantages of the suggested approach, which encompasses the usage of multiple strategies.

Key Contribution

• This method improves the data throughput and security, which are the special advantage of the two communication methods.

• It is also important that the sustainability of WBANs should be developed through energy-saving technologies. Adaptive sleep scheduling and dynamic transmission power are done using various techniques to manage and execute the reduction of energy consumption without compromising the integrity of data.

• High-speed and low-latency transmission in the integration of WBANs with high-speed and high-bandwidth communication networks including 5G and 6G also makes it possible to support real-time health monitoring and to implement telemedicine.

2. RELATED WORK

XIAO and HU [⁴⁵] designed the CP-ABE, an encryption solution for cloud storage systems based on the multi-permission ciphertext principle. In the standard paradigm, it seems safe from Chosen Plaintext Attacks (CPA). The method follows the hybrid encryption principle, whereby a CP-ABE scheme of symmetric key encryption will be used to offer security and a symmetric key to data encryption in order to retain efficiency. The traditional CP-ABE method can be improved in terms of decoding by replacing shortened typical pattern structures with AND/OR structures.

The ABE, along with data transfer techniques, encryption, privacy protection, and Wireless Multimedia Sensor Networks (WMSN) topology have been well studied by David and JAYACHANDRAN [¹¹]. This study covers issues including storage/computational costs, security, resilience, and ensuring reliability against cryptographic attacks. Storage and processing costs, as well as representation problems, arise when data is stored using cryptographic techniques, including key generation, encryption, and decryption [¹⁰]. Hash validators and trusted certificate authorities are used to generate and transmit key information to resolve these issues.

FAN et al. [¹²], demonstrated a secure and effective method for storing and accessing patient-related data over WBANs using multiple secret sharing strategies. VENKATASUBRAMANIAN et al. [¹³] Using CDMA modulation for WBAN ensures multi-layered security based on authentication methods and secure key management techniques [²].

SALEHIET et al.’s review [¹⁴] examines a key dissection of the two devices. Additionally, the principles for wireless channel attributes and the parameters needed for private key creation are suggested. Finally, they described several private key generation methods to enhance privacy and avoid information verification [¹⁵]. The proposed technique may swiftly produce 128 symmetric keys, improving WBAN’s service quality and enabling safe sensor-to-sensor communication.

The researchers found out that the security requirements and issues in every layer of a remote health monitoring system, so USMAN et al. [¹⁶] developed a new four-layer scheme. The article presents the literature of enhancing security and privacy in WBANs. Merging in vivo nanonetworks is one of the most difficult study areas in order to have an efficient remote health monitoring system. A strong WBAN ecosystem is needed to achieve end-to-end security. Moreover, the emphasis is on medical implants and nanonetworks, highlighting the challenges in establishing these ecosystems.

MALIK et al. [¹⁷] WBAN detection devices are prone to find themselves in insecure circumstances since they are employed to gather sensitive information hence they must have high security levels to prevent communications that have already been compromised by the system. There is a varying level of privacy and security that such solutions can offer to the private medical information of patients. The system is highly susceptible to compromise or misuse, as patient data is a crucial element in critical cases. WBANs, their architecture, and their uses in medical monitoring are first discussed in this article. The importance of cryptographic frameworks and algorithms in addressing security and privacy issues at different levels of the WBAN network is also discussed.

The University of Alabama Huntsville (UAH) WBAN infrastructure was described by WARREN et al. [¹⁸] that forms the context of the description of this study. In accordance with global values, including Bluetooth, and OpenMet, KSU creates wearable health monitoring devices. UAH researchers used TinyOS, a ZigBee-compatible wireless sensor platform with hardware-level encryption, to create these wearable activity and health monitors. To create this infrastructure, organizations like KSU and UAH must work hard to develop and use standardized terminology, interfaces, and security measures.

LIM et al. [¹⁹] evaluated the security risks of WBANs for remote health monitoring as a post-processing stage. The ideal system should be based on a scalable platform that needs minimal operator input after its design and implementation. Four pillars form the basis of the computer: (i) a wireless transmitter and two biosensors; (ii) a hardware module to create a WBAN; (iii) sending data over the Internet to a distant server; and (iv) Automation is used for data gathering, analysis, and reporting. The novel highlights the importance of taking an extra precaution when communicating or sharing medical records with medical professionals or when delivering medical care since the information stored within the systems is very sensitive.

AL-JANABI [²⁰] This paper discusses the WBAN communication framework, outlines the security and privacy requirements for WBANs, and identifies significant threats to their integrity. It also reviews advanced security practices and relevant research in the field and evaluates potential areas for future research and development. Medical professionals must protect these records from unauthorized access whenever they are used and stored. This protection can only be achieved through enhanced computer security and privacy. Consequently, addressing the security and privacy challenges posed by WBANs is essential.

BARUCH et al. [²¹] the Hybrid Encryption Algorithm (HEA) is a novel idea that can be used in wireless and wired networks. This data protection strategy considers the limitations of sensor networks, such as bandwidth, processing power, and dynamic topology.

CHANDRASEKARAN et al. [²²] ABE is a two-way symmetric algorithm and ciphertext technique that proposes safe and effective data communication in WBAN that has constant-size ciphertext. It equally provides control access enhancements and policy enhancements [²³]. Any alteration of the policy of WBAN in relation to the emerging information has not been dealt with in any studies.

Their study by Khan et al. [²⁴] demonstrated that a safe and effective ABE architecture can be developed using central encryption and delegation. This approach takes advantage of the lightweight properties of the Elliptic Curve Cryptography (ECC) algorithm and the syntax of CP-simple ABE where a user can always keep the computational costs constant when encrypting and decrypting data. The platform relies on inexpensive WBAN sensors that have low power consumption to store and retrieve personal health information on the cloud in a secure way [⁴, ²⁵]. The analyses of safety have been performed using some of the safety models employed, which reveal that the proposed strategy is safe when applied under the assumption of the ECDDH.

ZHOU et al. [²⁶] proposed an efficient information access control method for WBANs, which was developed using the RSA-based certificateless signature encryption scheme introduced in this study [²⁷, ²⁸]. This method is easy to implement in industry as it uses only the basic RSA cryptosystem, excluding bilinear pairs. However, it does not have any serious issues with certificate management or hosting.

The created physical nodes are applied as active nodes of the proposed Hierarchical Energy Efficient Secure Routing (HEESR) protocol [²⁹,³⁰,³¹]. To ensure security of the transmission, the data is encrypted with the aid of asymmetric encryption technology and squeezed with the help of the Huffman compression technology. It is unlike other conventional protocols since it employs the traffic priority data, such as the power levels and the overriding data and non-overriding data, to decide on the most optimal route.

The main objective of secure communication is primarily its security features, such as non-repudiation, tolerance, dependability, secrecy, and message integrity. In the case of IoT-based WBANs, an efficient and lightweight cross-domain access control system has been built [³², ³³]. This benefit technology applies identity-based signing on WBAN side and certificateless signing on the side of application provider. Compared to the use of RSA, bilinear layers, and ECC encryption systems, hyperelliptic curves need less factors, larger key sizes.

The attributes to the users of the ABE system who have attribute privileges are given a unique set of attributes and a private key of encryption [³⁴]. In order to effectively decode the ciphertext, the user requires an attribute private key and the properties of the user must correspond to the construction of the admission that is set by the data owner. Therefore, the ABE algorithm provides an easy and remarkably effective access control mechanism to protect data across WBANs in the cloud storage systems.

3. PROPOSED METHODOLOGY

For binary optimization difficulties, this learning proposes a new binary variant of fuzzy logic. Fuzzy logic is the one that considers cost in making decisions which nodes to use. Ant colony algorithm employs top-level nodes to choose the optimal route. The initial part in the development of a fuzzy system is identifying the input and output that will work best on the task being done. The drawbacks of this algorithm and the smooth convergence requirement require the utilization of fuzzy structures having two inputs and two outputs where parametric control of the convergence of the method is possible. The system requires two parameters: the number of repetitions and the fitness of the impartial purpose. From the pheromones and exploratory data, we obtain a fuzzy system that controls two outputs. The energy and the waiting traffic are examples of the system inputs. Wireless sensor networks do not have enough energy. These sensors need batteries to work hence. To solve this problem, fuzzy-based optimal routing protocols are introduced. The Merkle-Hellman algorithm has been used in many optimization techniques in previous research. The formulation of vague safety laws enables the assessment of whether MHKC is highly safe and impermeable. The calculation amount and calculation cost for different message sizes are reduced.

In this proposed system, the sparsity-based technology to compress matrix data and significantly reduce the loss of information accuracy. Merkle-Hellman is used for data encryption to improve security. To ensure high-quality results, reconstruct your data using the decomposition threshold approach. One of the techniques, which categorizes the mean rate within the environment as well as provides optimal routing, is hierarchical clustering. Figure 1 shows the proposed algorithm.

Figure 1
Overview of proposed methodology.

3.1. Hierarchical clustering

Since there are three ideas, numerous numerical or hierarchical categorization methods and procedures have been researched and assessed:

1. Developing the dataset,

2. Implement the clustering algorithm and calculate the similarity coefficient. Every stage has a separate process. Notable subgroups that serve as perfect illustrations of hierarchical groups include split groups and aggregation groups.

Both quantitative and qualitative data types are possible. Two varieties of correlation coefficients are available:

1. The dissimilarity coefficient

2. Similarity of the coefficient.

We focus on the framework of the key concepts of the hierarchical algorithms utilized in WBAN research rather than expanding into great depth regarding the suggested clustering and comparison-based approaches.

3.2. Data collection and compression

The cluster leader gathers data from the cluster’s member nodes after the nodes have been appropriately categorized. By compressing these files, less computing power is required to send them over the network. In recently published work, we refer to data collected over some time as a “data block.” According to the compression sensitivity principle [³⁵], data can be compressed if it exhibits regular sparsity properties in a specific domain. Let’s assume d-vector.

(1) $$\text{d}=ø\text{a}$$

Let’s assume ø ∈ P^z×z – foundation of the space’s measurements for sparseness. D and A are regarded as comparable if d’s U value is less than Z and more significant than 0. Each element of the observation matrix, Ö ∈ P^O×Z, is considered to be entirely independent and uniformly distributed if O is less than Z. Specifically, it can be applied to the K prototype model to model the data on power consumption using a monitoring vector that is acquired by:

(2) $$\text{b}=\ddot{\text{O}}\text{d}$$

Using the summation theorem, a data vector d can be created from the value of b if O is selected at random. It is necessary to choose a suitable method for filling in the compressed scattered data before defining the data production. Frequently utilize signals like Fourier and Wavelet Transforms and transfer them to a specific frequency domain [³⁶, ³⁷]. Furthermore, the wavelet transform is used to identify various loads, while the Fourier transform is ignored, but no studies have supported the use of data compression. Because the data being compressed and used here is not chronologically ordered. Morlet and Grossman developed an illustration of the wavelet shift.

(3) $$i.e.,{Ø}_{x,y}(w)=\frac{1}{\sqrt{x}}i\left(\frac{w-y}{x}\right)$$

Various types of wavelet transforms include Continuous Wavelet Transform (CWT), Discrete WT (DWT), Stationary WT (SWT), and Wavelet Packet Transform (WPT). According to the widely recognized definition of CWT. Let’s assume ø_x,y (w) – wavelet, x – scale factor, y – shift factor, and i(w – y/x) – offspring.

(4) $$T_{x,y}={\displaystyle {\int }_{-\infty }^{\infty }{Ø}_{x,y}}(w)d(w)fw$$

This study employs DWT to sparsify compressed data since it focuses on discrete data. By separating the volume and displacement components, DWT is computed as follows: Let’s assume d(w) – time series.

(5) $$\text{x}={\text{x}}_{\text{0}}^{\text{O}}$$

(6) $$\text{y}={\text{zy}}_{\text{0}}{\text{x}}_{\text{0}}^{\text{O}}$$

The following is obtained by entering the equation (4) above.

(7) $${\mathrm{t}}_{\mathrm{o},\mathrm{z}}={\mathrm{x}}_{\text{0}}^{-\frac{\mathrm{o}}{\text{2}}}{\displaystyle \sum _{\mathrm{u}}\mathrm{d}[\mathrm{u}]\mathrm{i}\left[{\mathrm{x}}_{\text{0}}^{-\mathrm{O}}\mathrm{z}-\mathrm{u}{\mathrm{y}}_{\text{0}}\right]}$$

Assign x₀ = 2, y₀ = 1 then the result becomes

(8) $${\mathrm{t}}_{\mathrm{O},\mathrm{z}}=2^{-\frac{\mathrm{O}}{\text{2}}}{\displaystyle \sum _{\mathrm{u}}\mathrm{d}[\mathrm{u}]\mathrm{i}\left[2^{-\mathrm{O}}\mathrm{z}-\mathrm{u}\right]}$$

DWT divides the actual signal into more precise and approximate components using a j[z]-high-pass filter and an i[z]-low-pass filter. Calculate the association amid the high-pass filter and the low-pass filter, as shown in Equation 9.

(9) $$\text{j}\left[\text{V}-\text{1}-\text{z}\right]={\left(-\text{1}\right)}^{\text{z}}\text{i}\left[\text{z}\right]$$

Data is compressed and encrypted for additional security using FWT according to bandwidth change and transmitted. Let’s assume V-length of the filter.

3.3. Encryption of data Merkle-Hellman algorithm

It explored the importance of security by suggesting Merkle-Hellman encryption for this process. Furthermore, security standards in WBAN must be met when transmitting health-monitored data observed from patients.

Data confidentiality: That resources only authorized people can discover the data submitted. The information is first encrypted using a private key and sent, identifying the security as symmetric or asymmetric.

Data authenticity: It validates data obtained from a specified source and generates a Message Authentication Code (MAC) using a shared secret.

Data integrity: Verify the MAC and safeguard that the received data is in good condition. Make sure all the information received is up-to-date and not reprinted from old messages that have been changed. To maintain an approach of the number of times a message is sent using popular techniques, add a counter. These conditions are met by encryption that uses the Markle-Heller-Mandelbrot (MHM) algorithm. The algorithm takes higher evaluation of cryptography key standard to convert the plain text into encrypted text using Soft logical evaluation.

Merkle-Hellman algorithm (MHA)

The fundamental idea of MHA encryption, which conceals the super-auto-incrementing feature through modular multiplication and substitution, can be used to address the substring problem. The security is plied by cryptography encryption which is generated from the adapted vector, and the key and decryption are estimated to be from the original, super-enhanced vector.

Mathematical Explanation

For a sequence to be considered a hyperbola, each successive number must be greater average mean rate of previous two numbers. Furthermore, select a arrangement of positive integers that increases in size each time.

(10) $$\text{q}=\left(\text{q1},\text{q2},\text{qZ}\right)$$

Converting the message to binary code is the next step. The letter b stands for a binary sequence. Selecting two integers—an integer (x) and a remainder (P)—that are both greater to sum of mean rate. The private key of the encryption system is formed by combining the sequence s and the numbers x and P. Manifold is formed by dividing block x, which consists of the elements q1, q2, q3…qZ of q by P.

(11) $$\text{rg}=\text{g}*\text{simod}\left(\text{x}\right)$$

The appropriate elements of the binary array x (r1, r2, r3, … , rZ) are multiplied by each element of the array r. The numbers are combined to create the encrypted message O_g. Let’s assume r-assigned value.

(12) $${\mathrm{O}}_{\text{g}}={\displaystyle \sum _{\mathrm{g}=1}^{\mathrm{z}}{\mathrm{r}}_{\text{g}}*{\mathrm{y}}_{\text{g}}}$$

The sequence O = (O₁, O₂, O₃…O_n) constitutes the cryptosystem public key. Using the Merkle-Hellman algorithm to encrypt the data to secure the content.

3.4. Optimal routing of data

Travel time, distance, and energy usage are all taken into consideration by the new routing model. A cost computation is used by fuzzy logic to determine which node is best. The ant colony algorithm selects the best course of action by looking at the top node. The first stage in creating a fuzzy system is selecting the appropriate inputs and outputs for the task’s specific conditions. Considering that this task is limited and the algorithm requires uniform convergence, the fuzzy structure has 2 Inputs and 2 Outputs as a typical parametric control of the method. The number of iterations to be performed and the fit value of the goal function are control parameters of the system generated by the pheromone and exploratory data. This fuzzy system operates the two outputs. The system’s inputs include things like energy and the amount of waiting traffic. A wireless sensor network’s power supply is constrained because these sensors need to be powered by batteries. By using this information, it is possible to choose the specific node that would be the most economically efficient in minimizing overall energy utilization [³⁸, ³⁹]. The fuzzy evaluation takes membership function takes a first-order radio-wave model to depict energy accurately. Prioritizing the nodes with the highest energy is made easier by using a first-order radio model. Let it be that packet p is communicated since the basis node to the destination node through the chosen nodes. In that case, energy is wasted, and only several nodes may die in response to the simulated number of rounds.

(13) $${\mathrm{E}}_{TI}={\mathrm{E}}_{\mathrm{e}\_pi}*t+{\in }_{amp}*g^a$$

The following equation gives this power law first-order model for radio emission. The equation for the initialized term representation. It contains the following parameters like distance, implication while getting the minimal bit error rate of 2–5.

(14) $${\mathrm{E}}_{e\_cx}=Vcc*\frac{I_{TP}}{t_{dat{a}_{rate}}}$$

The working voltage is represented by the Vcc parameter, the transmission current by the ITP parameter, and the data transfer rate by the t_{data_rate} rate parameter. The energy used to receive data is described by the following equation.

(15) $${\mathrm{E}}_{RI}={\mathrm{E}}_{e\_pi}*t$$

When the distance is fixed, it reveals that the energy to be used is by number active data bits (15). E_{e_pi} and E_RI are used in first-order radio models to compute energy dissipation. The time that elapses between a packet’s origin and its reception at its destination is given by the variable ‘p’ as the total packet count when fuzzy input is embedded into the traffic model. The less-popular node will have a higher probability of getting selected during the route selection process as the amount of the queue drops. The fuzziness levels are set to low, medium, and high, and the bits-per-second (bps) cap is maintained at a constant value between 0 and 10. The daily traffic volume is measured using the dimensionless Erlang unit. The quantity of data being transferred across a wireless network is known as the “offered load” or “transmitted load.” The fuzzily computed output of the system takes into account the price of each node. However, the cast node that would be finally chosen is the one that is most energetic but least busy or the one with the lowest traffic.

3.5. Reconstruction of data

(16) $$\widehat{d}=argmin{\Vert d\Vert }_{0}s.tj=\Phi d$$

In this case, $\widehat{d}$ stands for a parameter that represents estimated original data referred to as d. This parameter is equal to the d if the d is sparse in the wavelet domain which looks like,

(17) $$=argmin{\Vert i\Vert }_{0}s.tj=\Phi \Psi d$$

The sparse data in this case î requires estimation. acts as the wavelet basis in this investigation. Reconstruction, in essence od active data packets in transmission medium takes place in smart grid. The proposed work it is more effective it has received limited attention. It is also possible to state the r-squared of the correlation to Ψ and Φ in terms of

(18) $${\rho }_{\Psi ,\Phi }=\frac{co\nu (\Psi ,\Phi )}{\sqrt{D\Psi }\sqrt{D\Phi }}$$

and

(19) $$co\nu (\Psi ,\Phi )=\mathrm{E}((\Psi -\mathrm{E}(\Psi ))·(\Phi -\mathrm{E}(\Phi )))$$

Here, $E(\Phi )=0;\sqrt{D\Phi }=1;{\rho }_{\Psi ,\Phi }=0:0008$ for the gauss matrix, which is used for the normal distribution. Since the electrical data are reconstructed using both the Gauss observation matrix and the Daub wavelet sparse matrix, we can conclude that they are approximately uncorrelated.

Implementation Steps:

Step:1 Set up sensors for health data collection.

Step:2 Set sensors states (Active, Idle, Sleep).

Step:3 Activate sensor for specific health data monitoring.

Step:4 Choose modules in N and multiplier m such that gcd^{(m, n) = 1}.

Step:5 Create the public key by applying modular multiplication.

Step:6 Return private and public key.

Step:7 Convert data to binary format (health data in binary).

Step:8 Encrypt the binary data using the Merkle-Hellman public key.

Step:9 Apply modulo N to the encrypted message.

Step:10 Generate message authentication code using the private.

Step:11 Return encrypted data and MAC for transmission.

Step:12 Route the encrypted data through selected node.

Step:13 Use an optimal routing method.

Step:14 Transmit the encrypted message to the base station or dest transmit.

4. EXPERIMENT RESULT EVALUATION

This section explores the comparison of result with existing methods with various routing protocols called Accurate Data Transmission Network (CRADTN), and Fuzzy Based Routing (SFBR), Secured and Reliable Data Communication (SRDC) with carious environment aspects various deigned in NS2 simulation environment. Such he EDHMA contrasting approach produce different parameter evolution to produce the performance accuracy as well the proposed system higher performance evaluation.

The parameter considerations Packet Delivery Ratio defies the average number of packets delivered in mean time with actual low defined traffic ate with successful attempts within the time of evaluation.

Throughput (TP): signifying the speed at which information is successfully sent over a network over a predetermined amount of time. Bits per second (bps), kilobits per second (Kbps), megabits per second (Mbps), or gigabits per second (Gbps) are the most common units of measurement. The concept of throughput is essential for gauging the efficiency and effectiveness of communication systems, the formulated representation

$$\frac{Totalnumberofpacketsreceived}{time}$$

End-To-End Delay (EED): The average mean time taken to deliver he packet from source to destination without any tolerance in communication.

The environment testing parameters are evaluated based on the network type which is explained in the Table 1 and the Table 2 presents the findings of a comparison between the existing approaches and the recently announced techniques. This section provides a brief description of these measures. Dynamic Node Selection is when nodes in a network can join or leave the network and change their roles (for example, from cluster head to relay node) based on things like how mobile they are, how much energy they have, and how the network is doing. This lets the network change in real time to changing topologies, which makes sure that communication stays fast even when nodes relocate or the network load changes.

Table 1
Environment setup and variables.

Area	150*150
Number of nodes	150
Actual energy loaded	0.6 J
Based station location point	(75, 75)
Size of the packet	6000 bits
Energy unit	70 nJ/bit
T rr	150 Pj/bit/m2
Size of grid communication	150*150
Head dependent clusters	5
Node states	4(sleep, idle, supervisory, active)
Network Type mobility	Dynamic node selection
Interface medium	Wireless
MaC Protocol	IEEE 802.11
Antenna Model	Surf sensing O-type Antenna
Delay type	Traffic content bit

Table 2
EDMHA model performance comparison with previous approaches.

METRICS/TIME IN SECONDS		EDMHA	CRADTN	SRDC	SFBR	TMQOS
DELAY (ms)	2 4 6 8	0.0184566 0.10000356 0.32167879 0.481234468	0.03311415 0.2423645 0.53003696 0.64364558	0.05011415 0.3223645 0.65003696 0.78364558	0.15011415 0.4223645 0.75003696 0.88364558	0.20011415 0.6223645 1.0003696 1.2364558
LIFETIME (ms)	2 4 6 8	0.930001354 1.196667778 1.327780901 1.595542446	0.85954874 0.9923158 1.249521369 1.400012588	0.78954874 0.9023158 1.09521369 1.210012588	0.60954874 0.7723158 0.92521369 1.100012588	0.48954874 0.6423158 0.82521369 0.990012588
LOSS (sec)	2 4 6 8	0.07812324 0.11248990 0.19002456 0.390245689	0.09954874 0.2623158 0.341369 0.520012588	0.18954874 0.3423158 0.521369 0.690012588	0.28954874 0.4423158 0.62521369 0.790012588	0.36954874 0.6223158 0.85521369 1.00012588
PDR (sec)	2 4 6 8	1.4900245 1.6102046 1.7325678 1.9602345	1.434551 1.562132 1.675589 1.8368754	1.114551 1.292132 1.415589 1.7068754	1.014551 1.182132 1.305589 1.5968754	0.894551 1.062132 1.125589 1.4068754
THROUGHPUT (kbps)	2 4 6 8	1.17890025 1.460905323 1.810920054 2.4990432212	1.03894551 1.40062132 1.7325589 2.228754	0.91894551 1.25062132 1.6125589 2.068754	0.71894551 1.1062132 1.4125589 1.668754	0.50894551 0.8062132 1.0125589 1.2668754

4.1. End to end delay

The delay represents the actual mean point of packets deliver in actual traffic rate from source to destination in the communication medium. The end-to-end delay (validation) values utilizing the TMQOS, SFBR, SRDC, and CRADTN techniques are helpful in the earlier condition. They employ 1.2364558 ms, 0.88364558 ms, 0.78364558 ms, and 0.64364558 ms. With the EBMHA technique, the end-to-end delay value in the recently announced system is significantly decreased, consuming 0.481234468 ms. Therefore, the proposed technique produces incredibly good detection. The outcome shows that the projected technique provides better performance.

The box plot (Figure 2) demonstrates how the values for different models (EDMHA, CRADTN, SRDC, SFBR, and TMQOS) are spread out. EDMHA has a much wider spread with an outlier, which means that its outcomes are not always the same. On the other hand, CRADTN, SRDC, SFBR, and TMQOS all have reasonably tight distributions, which means that these models tend to perform the same way.

Figure 2
EDMHA model performance comparison with previous approaches.

4.2. Network lifetime

The network lifetime is a pivotal metric that reproduces the working feasibility of wireless networks, including energy-efficient routing protocols, load balancing among nodes, and adaptive data transmission techniques life­span could only be ascertained by estimating the amount of energy left in the network.

The contrast between the network lifespan of data delivers at the time of evaluation in all over mean performance of network represented the proposed system in Figure 3. In the first scenario, the network lifespan values are 0.990012588 ms, 1.100012588 ms, 1.210012588 ms, and 1.400012588 ms, respectively, due to the use of TMQOS, SFBR, SRDC, and CRADTN. Utilizing the EDMHA technique considerably extends the network lifespan value, even though it consumes 1.595542446 ms in the proposed system. Consequently, it has been shown that the suggested approach produces better detection. The results show that the suggested solution has excellent functioning.

Figure 3
Network life time.

4.3. Throughput

One essential indicator of the effectiveness of data transfer within a network is network throughput. Understanding and optimizing throughput is critical for maintaining high-performance networks that adequately support the needs of modern users and applications. A network that can produce a higher throughput is considered superior. Throughput is leisurely to estimate as bits/s or bps.

The valuation of the preceding and recently implemented systems in relations of communication metric is shown in Figure 4. The throughput numbers are represented exiting methodologies comparison with proposed system are TMQOS, SFBR, SRDC, and CRADTN approaches requiring data transfers at 8 seconds in different kbps rate produce the performance, respectively. By employing the proposed EDMHA technique, takes lower time a well compared to previous methods, the throughput value of the suggested system is significantly increased.

Figure 4
Network lifetime evaluation.

4.4. Packet delivery ratio

Packet delivery ratio is defined as the number of packets received by the target with success.

The results of comparing the packet delivery rates of the two systems are shown in Figure 5. The proposed system shows in that graph takes higher packet delivery ratio are all lower than those for the first scenario. With the recently presented technology, the PR rate is improved as well in EDMHA method, which takes 1.9602345 seconds. Consequently, it has been demonstrated that the novel approach is successful in detection. The results demonstration that the project application of the proposed system outperforms the previous one.

Figure 5
Throughput evaluation.

4.5. Packet loss ratio

Constructed on the average mean rate of packet loss, the throughput is measured as per the tolerance time dependant packet in transmission.

Figures 5, 6 and 7 compares the earlier and newly implemented systems by evaluating the packet loss during communication loss. During the aspects are measured by average mean ate of absolute mean error caused by other parameters compiled into total sending packets. The proposed approach produces high performance compared to the other system as well in low level packet loss rate up to EDHMA attains 0.39 lower than other methods. The proportional accuracy of new projected system takes less time to transmit without any loss rate compared to the other systems.

Figure 6
Performance of packet delivery ratio.

Figure 7
Packet loss ratio evaluation.

4.6. Encryption time

The security measure are depend on encryption time, which the data is taken to process to encrypt the content to secure with key generation. Having the proposed method reduce high performance evaluation compared to the other system like SFBR, TMQOS and other comparatives shown in Figure 8 proves the proposed system accuracy.

Figure 8
Encryption time (S) results.

4.7. Energy consumption

The transmission time (Figure 9) may be determined by computing the distance between two sensor nodes in the event of a posture change. Energy usage, trip distance, and time are all factors in the new routing approach. When deciding which node to employ, fuzzy logic takes costs into account. The top node is used by the ant colony algorithm to choose the best route. The first stage in developing a fuzzy system is determining which inputs and outputs are appropriate for energy consumption. Therefore, regardless of the data rate, the proposed EDMHA protocol’s energy levels keep consumption constant.

Figure 9
Performance comparison of energy consumption.

4.8. Computational cost

Figure shows the computational cost comparison of the proposed model with [²⁷, ³²] and state of art protocols. Compared with all protocols our proposed model is having lesser computational cost due its simple mathematical model for key generation in security system. The proposed solution does away with concerns around certificate administration and key storage in addition to the need for a secure connection. It makes processing and transmission faster. Furthermore, it has the potential for providing impenetrability and immunity from unauthorized intrusion.

This Table 3 compares the simulation time (in milliseconds) of various encryption methods and the proposed model. It highlights the computational efficiency of the proposed method relative to other encryption techniques, demonstrating its improved performance.

Table 3
Computational cost comparison.

VARIOUS METHODS	SIMULATION TIME (ms)
[27]	18
[32]	1.92
[34]	22.96
[25]	58.16
[23]	5.87
[40]	6.77
Proposed Model	1.61

4.9. Communication cost

An alternative metric for communication cost (overhead) is the amount of extra bits that are appended to a message. The extra number of bits for earlier systems is method-dependent when it comes to encryption [²⁷, ³²]. We implemented a method to enhance security in our suggested model. As a result, the communication cost may be improved by reducing the amount of extra bits in the message.

This Table 4 compares the extra bits (communication cost) added by different encryption methods. The proposed model shows a reduction in communication cost compared to other methods.

Table 4
Communication cost comparison.

VARIOUS METHODS	BITS
[27]	2988
[32]	1296
[34]	2528
[25]	2592
[23]	1888
[40]	1856
Proposed Model	1185

5. CONCLUSION

To conclude that the research introduces the Merkle-Hellman algorithm for data encryption and to improve security using a sparse-based data compression method. To provide high-quality results, the data was reorganized using a reordering entry strategy. In order to ensure efficient routing, hierarchical clustering techniques were designed to classify different nodes in the environment. The study utilized the NS2 simulated network topology, revealing that the proposed method ensured reliable and optimal packet transmission. In the future, there will be various encryption methods combined with other compression methods. Our team is thoroughly examining the performance of the new method. Once we have our findings, we’ll be able to create new algorithms to safeguard sensitive information. Future work will look on adding more encryption and compression methods and making dynamic encryption algorithms. The team is looking at how well these methods work and wants to come up with new algorithms to protect sensitive healthcare data in WBANs.

6. ETHICAL CONSIDERATIONS

This research did not involve human participants or animals, and hence, ethical approval was not required. No ethical committees or internal review boards were involved in this study.

DATA AVAILABILITY

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

7. BIBLIOGRAPHY

• ^[1] SHARMA, D., “Wireless health care monitoring system with data security and privacy”, International Journal of Research in Computer Engineering & Electronics, v. 2, n. 2, pp. 1–2, 2013.
• ^[2] PRAGADESWARAN, S., SUBHA, N., VARUNIKA, S., MOULISHWAR, P., SANJAY, R., KARTHIKEYAN, P., AAKASH, R., & VAASAVATHATHAII, E. “Energy efficient routing protocol for security analysis scheme using homomorphic encryption”, Archives for Technical Sciences, v. 2, n. 31, pp. 148–158, 2024. doi: https://doi.org/10.70102/afts.2024.1631.148.
  » https://doi.org/10.70102/afts.2024.1631.148
• ^[3] CROSBY, G.V., GHOSH, T., MURIMI, R., et al., “Wireless body area networks for healthcare: a survey”, International Journal of Ad hoc, Sensor & Ubiquitous Computing, v. 3, n. 3, pp. 1, 2012. doi: https://doi.org/10.5121/ijasuc.2012.3301.
  » https://doi.org/10.5121/ijasuc.2012.3301
• ^[4] ARAVIND, B., HARIKRISHNAN, S., SANTHOSH, G., VIJAY, J.E., & SARAN SUAJI, T. “An efficient privacy – aware authentication framework for mobile cloud computing”, International Academic Journal of Innovative Research, v. 10, n. 1, pp. 1–7, 2023. doi: https://doi.org/10.9756/IAJIR/V10I1/IAJIR1001.
  » https://doi.org/10.9756/IAJIR/V10I1/IAJIR1001
• ^[5] YAZDANDOOST, K.Y., Channel model for body area network (BAN), New York, IEEE, 2007.
• ^[6] LUTHRA, P., & PHADKE, K. “An energy conservation technique utilizing fpga in dynamic wireless sensor networks”, International Academic Journal of Science and Engineering, v. 11, n. 3, pp. 40–43, 2024. doi: https://doi.org/10.71086/IAJSE/V11I3/IAJSE1160.
  » https://doi.org/10.71086/IAJSE/V11I3/IAJSE1160
• ^[7] ROCKER, C., ZIEFLE, M., E-health, assistive technologies and applications for assisted living: challenges and solutions, Hershey, Medical Information Science Reference, 2011.
• ^[8] SOWMYA, C.S., VIBIN, R., MANNAM, P., MOUNIKA, L., KABAT, S.R., & PATRA, J.P. “Enhancing smart grid security: detecting electricity theft through ensemble deep learning”, In: 2023 8th International Conference on Communication and Electronics Systems (ICCES), pp. 1803–1810, 2023 June, IEEE. https://doi.org/10.1109/ICCES57224.2023.10192747.
  » https://doi.org/10.1109/ICCES57224.2023.10192747
• ^[9] SAHAI, A., WATERS, B., “Fuzzy identity-based encryption”, In: Proceedings of the Annual International Conference on the Theory and Applications of Cryptographic Techniques, pp. 457–473, 2005.
• ^[10] RADHIKA, K., & VELMANI, R. “Bluetooth and gsm based smart security system using raspberry pi”, IOP Conference Series: Materials Science and Engineering, v. 981, n. 4.
• ^[11] DAVID, D.S., JEYACHANDRAN, A., “A comprehensive survey of security mechanisms in healthcare applications”, In: Proceedings of the 2016 International Conference on Communication and Electronics Systems (ICCES), pp. 1–6, 2016.
• ^[12] FAN, R., PING, L.D., FU, J.Q., et al., “The new secure and efficient data storage approaches for wireless body area networks”, In: Proceedings of the 2010 International Conference on Wireless Communications & Signal Processing (WCSP), pp. 1–5, 2010.
• ^[13] VENKATASUBRAMANIAN, S., JOTHI, V., “Integrated authentication and security check with CDMA modulation technique in physical layer of wireless body area network”, In: Proceedings of the 2012 IEEE International Conference on Computational Intelligence and Computing Research, pp. 1–6, 2012.
• ^[14] SALEHI, S.A., RAZZAQUE, M.A., TOMEO-REYES, I., et al., “Efficient high-rate key management technique for wireless body area networks”, In: Proceedings of the 2016 22nd Asia-Pacific Conference on Communications (APCC), pp. 529–534, 2016.
• ^[15] FRAGOPOULOS, A.G., GIALELIS, J., SERPANOS, D., “Imposing holistic privacy and data security on person centric eHealth monitoring infrastructures”, In: Proceedings of the 12th IEEE International Conference on e-Health Networking, Applications and Services, pp. 127–134, 2010.
• ^[16] USMAN, M., ASGHAR, M.R., ANSARI, I.S., et al., “Security in wireless body area networks: from in-body to off-body communications”, IEEE Access, v. 6, pp. 58064–58074, 2018.
• ^[17] MALIK, M.S.A., AHMED, M., ABDULLAH, T., et al., “Wireless body area network security and privacy issue in e-healthcare”, International Journal of Advanced Computer Science and Applications, v. 9, n. 4, pp. 209–215, 2018.
• ^[18] WARREN, S., LEBAK, J., YAO, J., et al., “Interoperability and security in wireless body area network infrastructures”, In: Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 3837–3840, 2006.
• ^[19] LIM, S., OH, T.H., CHOI, Y.B., et al., “Security issues on wireless body area network for remote healthcare monitoring”, In: Proceedings of the 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, pp. 327–332, 2010. doi: https://doi.org/10.1109/SUTC.2010.61.
  » https://doi.org/10.1109/SUTC.2010.61
• ^[20] AL-JANABI, S., AL-SHOURBAJI, I., SHOJAFAR, M., et al., “Survey of main challenges (security and privacy) in wireless body area networks for healthcare applications”, Egyptian Informatics Journal, v. 18, n. 2, pp. 113–122, 2017. doi: https://doi.org/10.1016/j.eij.2016.11.001.
  » https://doi.org/10.1016/j.eij.2016.11.001
• ^[21] FAROOQ, S., PRASHAR, D., JYOTI, K., “Hybrid encryption algorithm in wireless body area networks (WBAN)”, In: Proceedings of the Intelligent Communication, Control and Devices, pp. 401–410, 2018. doi: https://doi.org/10.1007/978-981-10-5903-2_41.
  » https://doi.org/10.1007/978-981-10-5903-2_41
• ^[22] CHANDRASEKARAN, B., BALAKRISHNAN, R., NOGAMI, Y., “TF‐CPABE: an efficient and secure data communication with policy updating in wireless body area networks”, ETRI Journal, v. 41, n. 4, pp. 465–472, 2019. doi: https://doi.org/10.4218/etrij.2018-0320.
  » https://doi.org/10.4218/etrij.2018-0320
• ^[23] OMALA, A.A., MBANDU, A.S., MUTIRIA, K.D., et al., “Provably secure heterogeneous access control scheme for wireless body area networks”, Journal of Medical Systems, v. 42, n. 6, pp. 108, 2018. doi: https://doi.org/10.1007/s10916-018-0964-z. PubMed PMID: 29705947.
  » https://doi.org/10.1007/s10916-018-0964-z
• ^[24] KHAN, S., IQBAL, W., WAHEED, A., et al., “An efficient and secure revocation-enabled attribute-based access control for eHealth in smart society”, Sensors, v. 22, n. 1, pp. 336, 2022. doi: https://doi.org/10.3390/s22010336. PubMed PMID: 35009878.
  » https://doi.org/10.3390/s22010336
• ^[25] LI, F., HAN, Y., JIN, C., “Cost-effective and anonymous access control for wireless body area networks”, IEEE Systems Journal, v. 12, n. 1, pp. 747–758, 2018. doi: https://doi.org/10.1109/JSYST.2016.2557850.
  » https://doi.org/10.1109/JSYST.2016.2557850
• ^[26] ZHOU, Y., ZHANG, Y., HAN, Q., et al., “Backdoor-resistant identity-based proxy re-encryption for cloud-assisted wireless body area networks”, Information Sciences, v. 604, pp. 80–96, 2022. doi: https://doi.org/10.1016/j.ins.2022.05.007.
  » https://doi.org/10.1016/j.ins.2022.05.007
• ^[27] LIU, X., WANG, Z., YE, Y., et al., “An efficient and practical certificateless signcryption scheme for wireless body area networks”, Computer Communications, v. 162, pp. 169–178, 2020. doi: https://doi.org/10.1016/j.comcom.2020.08.014.
  » https://doi.org/10.1016/j.comcom.2020.08.014
• ^[28] AHMADZADEH, S., “Study of energy-efficient biomedical data compression methods in the wireless body area networks (WBANs) and remote healthcare networks”, International Journal of Wireless Information Networks, v. 30, n. 3, pp. 252–269, 2023. doi: https//doi.org/10.1007/s10776-023-00599-6
  » https://doi.org/10.1007/s10776-023-00599-6
• ^[29] NARANJO, P.G.V., SHOJAFAR, M., MOSTAFAEI, H., et al., “P-SEP: a prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks”, The Journal of Supercomputing, v. 73, n. 2, pp. 733–755, 2017. doi: https://doi.org/10.1007/s11227-016-1785-9.
  » https://doi.org/10.1007/s11227-016-1785-9
• ^[30] ROSHINI, A., KIRAN, K.V.D., “Hierarchical energy efficient secure routing protocol for optimal route selection in wireless body area networks”, International Journal of Intelligent Networks, v. 4, pp. 19–28, 2023. doi: https://doi.org/10.1016/j.ijin.2022.11.006.
• ^[31] PATHAK, V., SINGH, K., CHANDAN, R.R., et al., “Efficient compression sensing mechanism based WBAN system using blockchain”, Security and Communication Networks, v. 2023, n. 1, pp. 8468745, 2023. doi: https://doi.org/10.1155/2023/8468745.
  » https://doi.org/10.1155/2023/8468745
• ^[32] ULLAH, I., ZEADALLY, S., AMIN, N.U., et al., “Lightweight and provable secure cross-domain access control scheme for internet of things (IoT) based wireless body area networks (WBAN)”, Microprocessors and Microsystems, v. 81, pp. 103477, 2021. doi: https://doi.org/10.1016/j.micpro.2020.103477.
  » https://doi.org/10.1016/j.micpro.2020.103477
• ^[33] VAKIL, M.H., SHIRMOHAMMADI, Z., “EDC-ER: an efficient data compression method for energy reduction in WBANs”, IEEE Access, v. 12, pp. 155274–155286, 2024.
• ^[34] LI, F., HONG, J., “Efficient certificateless access control for wireless body area networks”, IEEE Sensors Journal, v. 16, n. 13, pp. 5389–5396, 2016. doi: https://doi.org/10.1109/JSEN.2016.2554625.
  » https://doi.org/10.1109/JSEN.2016.2554625
• ^[35] SUN, J., YU, Y., WEN, J., “Compressed-sensing reconstruction based on block sparse Bayesian learning in bearing-condition monitoring”, Sensors, v. 17, n. 6, pp. 1454, 2017. doi: https://doi.org/10.3390/s17061454. PubMed PMID: 28635623.
  » https://doi.org/10.3390/s17061454
• ^[36] YAPAR, O., BASU, P.K., VOLGYESI, P., et al., “Structural health monitoring of bridges with piezoelectric AE sensors”, Engineering Failure Analysis, v. 56, pp. 150–169, 2015. doi: https://doi.org/10.1016/j.engfailanal.2015.03.009.
  » https://doi.org/10.1016/j.engfailanal.2015.03.009
• ^[37] DAS, S., SETHY, R.R., “Image compression using discrete cosine transform & discrete wavelet transform”, D.Sc. Thesis, National Institute of Technology, Silchar, India, 2009.
• ^[38] SARAVANAN, N., RAMACHANDRAN, K.I., “Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN)”, Expert Systems with Applications, v. 37, n. 6, pp. 4168–4181, 2010. doi: https://doi.org/10.1016/j.eswa. 2009.11.006.
  » https://doi.org/10.1016/j.eswa.2009.11.006
• ^[39] TABATABAEI, S., RAJAEI, A., RIGI, A.M., “A novel energy-aware clustering method via lion pride optimizer algorithm (LPO) and fuzzy logic in wireless sensor networks (WSNs)”, Wireless Personal Communications, v. 108, n. 3, pp. 1803–1825, 2019. doi: https://doi.org/10.1007/s11277-019-06497-6.
  » https://doi.org/10.1007/s11277-019-06497-6
• ^[40] GAO, G.M., PENG, X.G., JIN, L.Z., “Efficient access control scheme with certificateless signcryption for wireless body area networks”, International Journal of Network Security, v. 21, pp. 428–437, 2019.
• ^[41] XING, J., ZHU, Y., “A survey on body area network”, In: Proceedings of the 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1–4, 2009.
• ^[42] WANG, J., ZHANG, Z., XU, K., et al.,”A research on security and privacy issues for patient related data in medical organization system”, International Journal of Security and Its Applications, v. 7, n. 4, pp. 287–298, 2013.
• ^[43] YUCE, M.R., KHAN, J., (eds), Wireless body area networks: technology, implementation, and applications, Boca Raton, CRC Press, 2011.
• ^[44] LI, M., YU, S., ZHENG, Y., et al., “Scalable and secure sharing of personal health records in cloud computing using attribute-based encryption”, IEEE Transactions on Parallel and Distributed Systems, v. 24, n. 1, pp. 131–143, 2013. doi: https://doi.org/10.1109/TPDS.2012.97.
  » https://doi.org/10.1109/TPDS.2012.97
• ^[45] XIAO, M., HU, X., “Multi-authority attribute-based encryption access control scheme in wireless body area network”, In: Proceedings of the 2018 3rd International Conference on Information Systems Engineering (ICISE), pp. 39–45, 2018.

Publication Dates

• Publication in this collection
  30 Jan 2026
• Date of issue
  2026

History

• Received
  06 May 2025
• Accepted
  12 Nov 2025