Due to the advancement in wireless technology and miniaturization, Wireless Body Area Networks (WBANs) have gained enormous popularity, having various applications, especially in the healthcare sector. WBANs are intrinsically resource-constrained; therefore, they have specific design and development requirements. One such highly desirable requirement is an energy-efficient and reliable Data Aggregation (DA) mechanism for WBANs. The efficient and reliable DA may ultimately push the network to operate without much human intervention and further extend the network lifetime. The conventional client-server DA paradigm becomes unsuitable and inefficient for WBANs when a large amount of data is generated in the network. Similarly, in most of the healthcare applications (patient's critical conditions), it is highly important and required to send data as soon as possible; therefore, reliable data aggregation in WBANs is of great concern. To tackle the shortcomings of the client-server DA paradigm, the Mobile Agent-Based mechanism proved to be a more workable solution. In a Mobile Agent-Based mechanism, a task-specific mobile agent (code) traverses to the intended sources to gather data. These mobile agents travel on a predefined path called itinerary; however, planning a suitable and reliable itinerary for a mobile agent is also a challenging issue in WBANs. This paper presents a new Mobile Agent-Based DA scheme for WBANs, which is energy-efficient and reliable. Firstly, in the proposed scheme, the network is divided into clusters, and cluster-heads are selected. Secondly, a mobile agent is generated from the base station to collect the required data from cluster heads. In the case, if any fault occurs in the existing itinerary, an alternate itinerary is planned in real-time without compromising the network performance. In our simulation-based validation, we have found that the proposed system delivers significantly improved fault-tolerance and reliability with energy-efficiency and extended network lifetime in WBANs.
The rapid growth of WBAN technology has enabled the fast and efficient acquisition and exchange of huge amounts of data in specialized fields. WBAN is a specialized network intended to connect various bio-sensor nodes located on or inside the human body. This network has a variety of tremendous applications; however, the healthcare applications of WBAN are very prominent. A WBAN provides long-term health-care monitoring of a patient without any interruption of normal daily life activities. This technology is a fast and efficient way to diagnose the patient and, at the same time, to consult the doctor or other medical staff for any required action to be taken [ The efficient DA technique reduces the ratio of traffic; consequently, energy-efficiency is improved. Efficient DA supports to improve robustness in the WBANs. Efficient DA techniques support to minimize the ratio of redundant data packets in the Network
Over the time, various schemes and techniques have been proposed for efficient DA in WBANs. The most common lies in the categories of cluster-based and mobile agent-based schemes. In WBAN, the bio-sensors related to a single body are brought together into a cluster. In collaborative-WBANs, data are aggregated and considered from multiple bodies. Thus collaborative-WBANs proposed a platform for efficient aggregated data exchange [
In WBANs, bio-sensor nodes regularly generate vital signs. These vital signs are further communicated to the BS regularly. The exceeded data caused reducing the throughput and reliability of the network. Thus DA approach is used for efficient data gathering. The bio-sensors nodes happen in the path towards the BS do data fusion. The accumulated data is transmitted to the BS; thus, latency inside the network is minimized. However, the DA methods sometimes reduced the accuracy level of the communicated data. Depending on the DA function, sometimes, the original data has not recuperated at the BS. Therefore, information accuracy may be lost. Therefore, a trade-off is required between DA techniques and desirable data accuracy. Thus, we proposed an energy-efficient and reliable Mobile Agent-based DA (MADA) scheme for WBAN. The novelty of the MADA scheme is a hybrid scheme in which both approaches are used in a single scheme, Clustering is used for itinerary planning while the mobile-agent approach is used for data aggregation in WBANs. This is the main contribution and worth of the research work. The MADA scheme aims to increase energy-efficiency and reliability inside the network and to reduce the end-to-end delay.
To minimize excessive data forwarding from nodes to MS, we proposed a mobile agent-based DA scheme. The scheme is highly convenient in WBANs domain ensuring reliable connectivity. Additionally, the proposed scheme provides efficient DA services to the WBANs in term of packets delivery ratio and delay in life-threatening emergency applications. The main contributions of this work are given as follows:
We ensured efficient reliable communication in WBAN to manages data forwarding among bio-sensors nodes, Gateway (GW) and MS. We maximized the service delivery ratio through efficient scheduling and collecting the desired critical data, which increase network throughput. We proposed itinerary planning for efficient DA of critical data, which minimizes the average end-to-end delay inside the network. furthermore, in the cause of fault in the existing itinerary, alternate itinerary planning is proposed to increase fault-tolerance inside the network.
The rest of the paper is organized as follows. In section 2, we briefly review the most relevant schemes related to the research problem mention above, particularly in the domain of WBAN. In section 3, the proposed scheme is demonstrated for the WBAN scenario. In section 4, the proposed scheme performance results are presented. And lastly, in section 5, the conclusion and future trends are given.
In this section, the widely used and cited DA schemes in WBANs are critically reviewed. The two well-known classes of approaches for DA in WBAN are
In Culpepper et al. [
Any-Body [
Sasirekha et al. in [
Reddy et al. [
Liu et al. [
Aiello et al. [
Fissaoui et al. [
In WBAN the generated data are heterogeneous in nature. The distribution of bio-sensors is also not uniform. The research study shows that DA using a cooperative cluster-based approaches have significant impacts on the efficiency and reliability of data gathering and communication in WBANs [
The main motivation behind our proposed MADA (Mobile Agent-based Data Aggregation) scheme is real-time data aggregation. In the proposed scheme, the idea of Mobile Agents (MA) is used to collect the data instead of the direct client-server paradigm. MA is a special type of software entity that migrates among CHs in WBAN to collect the sensed data from different bio-sensors [
Furthermore, the reliability of data also degrades in WBAN [
In the proposed system model, we consider WBAN-based healthcare in which the patients are being monitored in the remote hospital environment. The network model consists of eight bio-sensor nodes on a single body; mostly on-body bio-sensors are positioned on the human body. For a broad view, three bodies are considered, and each has eight bio-sensors. Each is fitted on their specified location on the entity body in a 30 × 30 meter square feet area. There is a medical server (MS), accessed by a trusted entity, and Sensor Nodes (SNs) [
Set of sensing nodes SN= (S1, S2,...,S8), which sense the vital sign from the human body such as ECG sensor, EMG sensor, and motion sensor. The sensed information is further reported to the aggregator node and then to the medical server.
Aggregator node is connected to both MS and SNs, collected vital signs from SNs, and communicating them to the MS through CHs as shown in
MS is a remote medical healthcare monitoring entity where the medical decision has been carried out. MA is also initiated from the MS entity, which travels through all CHs in a predefined itinerary path [
The operations of the proposed scheme are as: First, the bio-sensors related to each body are considered as a group or cluster. For each cluster, a CH is elected according to standard LEACH protocols with little modification of the requirement of WBANs. Itinerary planning between different CHs is established by using a minimum spanning tree (MST) [
The proposed model consists of three entity-bodies, as shown in
Variable | Meaning |
---|---|
Maximum transmission range of Bio-sensors | |
Total numbers of Bio-sensors | |
The set of all Bio-sensors | |
The sets of all cluster headers (CH's) | |
There itinerary of MA among CHs | |
The remaining energy of Bio-sensors nodes | |
Cluster Head |
In this section, the detailed procedure is explained for cluster formation and CH selection in WBAN. All bio-sensors fitted on a single are within the CN range, and their facilitated entity is single. Furthermore, there is trust between all bio-sensors of an entity-body. Therefore, we consider the group of bio-sensors within in single body as a cluster. Similarly, all the bio-sensors of each body within the WBANs are grouped into separate clusters, as shown in
According to the LEACH protocol, each bio-sensor node had to assign a rank based on the remaining energy and distance to the sink [
After the cluster formation and CH selection phase, the next phase is to perform the itinerary planning for MA migration amongst the CHs. For this task minimum spanning tree (MST) has been used. The MS has all the information of bio-sensors i.e., nodes position and their respective coordinates. Therefore, the MS can calculate the weight between CHs. For weight calculation between CHs, we used the following equation.
Wmax Rmax denotes the maximum transmission range. Let us consider x and y to be the two CHs. In the network, and
In this phase, a fault-tolerance based alternate itinerary is planned for data communication from specific bio-sensor nodes and clusters to the BS. The aggregated data are then further transmitted to the MS. This section's key contribution is a fault tolerance-based energy-efficient algorithm for DA in WBAN named Cooperative-FTEA. In which the itinerary is adjusted according to the priority level of the vital sign of efficient DA. As we know that CHs are liable to error because CH is selected from the group of SNs where any error may occur [
In some cases, the alternate itinerary is close to the selected typical itinerary that is no longer active. The key reason for that is due to failure in the first itinerary. If a node is no longer active in a cluster, it will not include in the itinerary. Additionally, organizing the alternate itinerary for MA traveling increases the fault-tolerance within the network as shown in
After organizing the network into clusters and electing a CH for each cluster, an itinerary is a plan among CHs. The BS initiated the MA to collect the data from CHs. Whenever MA visits a cluster for the first time, CHs notify their connected nodes for data collection activities. After visiting all CHs with the network, MA starts collecting data from the last CH and moving back to the BS where data are save in their related MS.
In this section, the operations of MA-Based DA are explained with the help of a flow chart. The detailed flowchart shows how the overall procedure is performed. First of all, the network is deployed, then the clustering of bio-sensors and CH selection is carried out. The next step is MA travel from the sink across all CHs, and finally, alternative itinerary planning is accomplished if in the established path or a node is found erroneous. The flowchart of the proposed scheme is given in
The algorithm for the proposed scheme is developed phase-wise. First, the network is deployed, and CH becomes elected accordingly. After this step, the itinerary is plan and MA is initiated from BS. Finally, for fault-tolerance alternate itinerary planning in case of node or link failures. The detailed algorithmic steps are given below.
Step1: Deploy bio-sensors (SN1, SN2, SN3, …… SNn)
Step 2: Group of bio-sensors related to a WBAN are made into a cluster ‘C’, and a
leader is elected for each know as CH
Step 3: Bio-sensor aggregated data transmission (
Step 4: Calculate
Step 5: Otherwise, Start from Step 1
Step 6: Initially,
Step 7: while
Step 8: Set
Step 9: Sink node generated MA to collect the aggregated data
Step 10:
Step 11: Route
Step 12: if
Step 13: if
Step 14:
Step 15:
Step 16: end if
In this section, the simulation setup of the proposed MA-based real-time energy-efficient DA is discussed. In the simulation setup, Bio-sensor nodes are uniformly distributed in the 5 × 5 m2 area. Where SNs are fitted on three patients. Each patient considers a cluster that consists of eight patients. The network topology is set as
Parameter | Value |
---|---|
Number of Bio-Sensors | 24 |
Monitoring Field magnitude | 5 × 5 m2 |
Energy spent by MA (Data Aggregation) | 15 nJ |
MA immediate delay | 10 ms |
MA Processing Delay | 50 ms |
MA Payload | 1024 bytes |
Collected data size at each CH | 200 bytes |
Aggregation coefficient | 1 |
Initial Energy of (WBAN) | 8 J |
In this section, the proposed MADA scheme is evaluated using various performance metrics through a simulator. The initial energy of WBAN is considered 8 J. The proposed MADA scheme is evaluated and compared with DAP-DS [ Average execution time (AET) Energy consumption Travelled distance (Itinerary length) Packet Delivery Ratio (PDR) End-to-End Delay
The above algorithm is used for MA-based efficient DA. Many notations have been used in this algorithm, some of which are elaborated here.
It is also called runtime or latency of specific operation. In the proposed solution, AET is the time MA travels across all CHs plus the time the MA returns to the sink. AET is the time that specific active bio-sensor spend while performing this specific operation.
It can be seen in
In this experiment, the reduced data transmission MA-based clustering approach has been used. Energy consumption is defined as that how much energy is consumed by applying this algorithm. In this experiment, the basic radio model is used for bio-sensor nodes required for transmitting and receiving data [
Similarly, the receiving of m-bits can be calculated as:
In
Itinerary length is the distance MA traverse from MS to all CHs and returns to the MS. It is calculated according to the routing table, where the distance between nodes is given. This is important so that to identify the smallest itinerary to the MS from SNs. The smallest itinerary length path is considering for data forwarding in the network. The smallest itinerary length shows less resource utilization because the most resource is utilized during communication in WBANs.
In this experiment, PDR is analysed of the proposed MADA scheme, DAP-DS [
In this experiment, the End-to-End delay is analysed using the proposed MADA scheme and EFTA DAP-DS protocols. E2E delay is the time to send a packet to reach from source to the destination. In WBAN, critical data related to the patient healthcare deal needs minimum delay and time to be delivered to the MS. E2E delay is calculated as when the first packet arrived at the sink in data communication in WBANs [
In
From
On the other hand, EFTA used the same strategy adopted by the proposed scheme. However, the clustering strategy of the proposed scheme is more energy efficient. DAP-DS encounter longer distance, as distance directly influences the delay; with the increase of distance, E2E delay also increases.
In WBANs healthcare applications, delay-sensitive data related to emergency-related services need reliable communication. This paper proposed a novel MADA scheme with agent-based DA in WBANs to improve reliability in WBANs. The proposed MADA scheme increases energy-efficiency, ensures reliable data delivery of critical data packets in healthcare applications. For itinerary planning clustering-based approach is used. Whenever the itinerary is planned, MA travel from MS to all CHs in the network. MA starts collecting aggregated data from the last CH, returning and collecting the aggregated data from all CHs. Whenever an itinerary becomes faulty, an alternate itinerary is planned at runtime, which increases fault-tolerance inside the network. From the simulation results, it is concluded that the proposed scheme outperforms the two benchmark schemes (i.e., EFTA and DAP-DS) and is proven to be an efficient and reliable DA solution for WBANs. The proposed scheme is energy-efficient because it consumes less energy, achieved low end-to-end delay, and increased the packet delivery ratio. Furthermore, fault-tolerance in the network is increased because the alternate itinerary is planned in the cause of failure occur in the current path or a node becomes decline.
In the future, we aim to utilize the sensor cooperation approach with itinerary planning for efficient data aggregation. Then, considerably fault-tolerance and reliability can be achieved with less amount of energy consumption with maximum throughput. The same approach can be adopted for IoT-based WBANs applications.