Considering Wireless Sensor Networks (WSNs) in today’s scenario, sending and receiving uninterrupted sensory data remains a challenge to achieve with minimal latency and energy consumption as low as possible. Energy consumption is exponentially growing in computing devices such as computers, embedded systems, portable devices, and wireless sensor networks. Extensive research has been in practice recently to minimize energy consumption without compromising the Quality of Service (QoS) that is to provide data to the requester node with minimum Delay and high Reliability. In this paper, a cooperative caching algorithm is used with the proposed Distributed Energy Aware Routing (DEAR) protocol that attempts to minimize energy consumption by reducing the packet overhead in the network and also providing the data to the requester with minimum delay by retrieving requested datum from the nearby caching node available in the vicinity of the requester or sink node. The simulation results clearly show that the energy consumption is less when the grid-based analytical model is used against the star/cluster based model while keeping the same necessary attributes.
ZigBee is a network and application layer specification based on the IEEE 802.15.4 MAC and PHY standard that is developed by the multi-vendor consortium, ZigBee Alliance. The ZigBee network layer supports the star, tree and mesh topologies. ZigBee based mesh networks are more suitable for practical applications, including home automation, intelligent transportation systems, personal healthcare, military communications, and so on. ZigBee device supports low data rate but exhibits enhanced energy consumption due to transceiving data packets, idle listening, overhearing, collisions and packet overheads due to multicast [
In this paper, we analyze the proposed DEAR routing protocol with energy usage in ZigBee and present an improved solution for achieving better routing and energy efficiency by incorporating the localized caching algorithm that is the Caching in Cooperative Zones (CCZ) to overcome the limitation of AODV for energy efficiency usage. The rest of this paper is organized as follows. In Section 2, the previous works related to the proposed scheme is discussed. Later in section 3 the proposed DEAR routing protocol in ZigBee is presented, and then our simulation model and parameters are discussed in Section 4. In Section 5, the various simulation results are presented and also we compare the simulation results of the DEAR_CCZ with the existing AODV_CCZ. Conclusions are drawn in the last section.
Various cache related schemes have been proposed in the past until P. Charan et al. [
J. Xu et al. [
T.P. Sharma et al. [
The DEAR protocol is an energy aware routing protocol that is an extension to the popular AODV routing protocol. The route discovery process in DEAR is different from AODV. The DEAR protocol is capable of generating routing paths based on the residual energy of nodes in the sensor network. DEAR calculates the routing path based on the Threshold value that is set in the protocol as 80% of the Average Energy of the Network. The Average energy of the network depends on the Total Link Cost that appears between two nodes “i” and “j” while transmitting data from the source node “i” to the destination node “j” as shown below in
The estimation of the Total Link Cost can be achieved in the following manner wherein, a sensor network based on the IEEE 802.15.4 standard is considered in which the traffic associated with the i-th mote be “Ti” packets per second (pps). Let us assume that a data flow “l” carries data traffic at a rate of “tl” pps, then Ti can be estimated as:
T i = ∑ l ∈ D ( i ) t l (1)
where, Di à denotes the set of data flows through node “i”.
The two important metrics for performance evaluation of a sensor network based on IEEE 802.15.4 are 1) Power Consumption, and 2) Quality of Services (QoS). Let P i ( T ) and Q i ( T ) be the power requirement and the Quality of Service requirements of a particular node “i” in the network when it carries T(pps).
If a new stream of data “m” is added to node “i” then this will result in a change in power consumption and QoS at that node.
Assuming that p i ( T ) be the instantaneous power consumption by the i-th
node when it carries “T” packets per second that includes all aspects of packet processing: storing, routing and forwarding them through the neighboring nodes.
We now define the power cost asscoiated with the m-th data stream as p i m ( t m , T i ) at node “i” which represents the power consumption of the m-th flow in combination with the power associated with other flows. So,
p i m ( t m , T i ) = a p i ( T i + t m ) + b [ p i ( T i + t m ) − p i ( T i ) ] (2)
where; a , b ≥ 0 . The first term is total power in (mW) due to adding the m-th flow, multiplied by some constant a; whereas, the second term represents the increase in energy for the other flows, multiplied by some constant b.
Hence the total power cost functions for the m-th data flow of rate “tm” on a routing path “λ(i)” starting from node “i” is written as:
P λ ( i ) m ( t m , T λ ( i ) ¯ ) = ∑ n ∈ λ ( i ) P n m ( t m , T n ) (3)
Similarly, the Quality of Service criterion, such as packet loss, delay or Packet Delivery Ratio (PDR) is given by
Q λ ( i ) m ( t m , T λ ( i ) ¯ ) = ∑ n ∈ λ ( i ) Q n m ( t m , T n ) (4)
where T λ ( i ) ¯ = ( T n 1 , T n 2 , ⋯ , T n | λ ( i ) | ) where n1 = i and nj are successive nodes in the routing path within the range 1 ≤ j ≤ | λ ( i ) | .
Since, DEAR protocol is expected to minimize the overall cost of power consumption while satisfying the requested QoS, therefore, it is essential to optimize the total energy cost attribute, TECi which will be a resultant of the power and the Quality of Service constraint. Hence, the total energy cost, TECi will be represented as:
T E C i = P λ ( i ) m ( t m , T λ ( i ) ¯ ) + γ Q λ ( i ) m ( t m , T λ ( i ) ¯ ) (5)
where, γ > 0 is a constant that matches the delay units with respect to power.
A sensor node will only be added to the routing path in a Distributed Energy Aware Routing (DEAR) protocol if the Threshold attribute is considered as a checkpoint. The node with 80% or more residual energy of the total energy cost will be included in a probable routing path. Thus, for a data to be sent from source node “i” to the destination node “j” the energy of the battery of nodes falling in its routing path must possess the minimum required threshold energy which is represented by
Threshold = 0.8 × T E C i j (6)
The flowchart of the DEAR routing protocol is shown in
The proposed CCZ algorithm is simulated on NS-2 (version 2.32). The routing protocols used is DEAR for routing data traffic in a WSN based on the IEEE 802.15.4 PHY and MAC protocols and a free-space propagation model as a radio propagation model. The number of nodes in the network is 16, and it is deployed in
a 100 × 100 square meter sensor area. The wireless bandwidth is 250 kbps, which represents the maximum amount of data or bits that can be transmitted over a period of time, typically one second. The other simulation parameters are illustrated in
In Star (Cluster based) network consists of 16 sensor nodes based on IEEE 802.15.4 PHY and MAC standard are considered which are all Full Function Devices (FFDs) and are accompanied with a fixed amount of cache memory to store the sensed data as shown in
In Grid (peer-to-peer) based network, sixteen sensor nodes are placed equidistant in an area of 100 m × 100 m, and all devices are IEEE 802.15.4 PHY and MAC compliant, as shown in
The IEEE 802.15.4 based sensor motes can generally be in one of the following modes: Idle, Transmit Mode, or Receive Mode. In the simulation we set the BO = 3 and the SO = 2, for which the Duty Cycle comes out to be:
Dutycycle = 2 − ( BO − SO ) = 2 − ( 3 − 2 ) = 2 − 1 = 1 2 = 0.5 or 50%. A sensor node with very low duty cycle goes to sleep mode until a beacon signal arrives again to wake up the radio [
The following three performance metrics have been evaluated:
1) Average Query Latency ( T q a v g )
The query latency “ T q ” can be defined as the time interval between a query sent by a requester and the response received back by the requester/sink. The average query latency is the query latency “ T q ” averaged over all the generated queries.
2) Byte Hit Ratio (B)
The byte hit ratio B is defined as the ratio of total bytes of data retrieved from the cache to the total number of requested data bytes by the sink/requester node. The byte hit ratio B includes bytes retrieved from a local hit known as a Local Byte Hit (Blocal); bytes retrieved from a zone hit known as a Zone Byte Hit (Bzone) and the bytes retrieved from a remote hit known as a Remote Byte Hit (Bremote). It is to be noted that bytes retrieved from a Global hit are freshly sensed data and
Parameter | Default Value | Range |
---|---|---|
Number of Nodes | 16 | |
Number of Data Items | 500 | |
Payload Size | 64 bytes | |
PHY and MAC Layer | IEEE 802.15.4 | |
Channel Frequency | 2.4 GHz | |
Bandwidth (kbps) | 250 | |
Waiting interval (tw) | 10 s | |
TTL | 300 s | 100 - 300 s |
Cache Size (KB) | 800 | 200 - 1400 |
Traffic Type | CBR | |
Routing Protocol | DEAR | |
Beacon Order | 3 | |
Superframe Order | 2 |
The data items are updated at the source nodes. The source node serves the requests on First-Come-First-Serve (FCFS) Basis.
cannot be considered to have been retrieved from the cache.
3) Total Energy Consumption (Etotal)
Total energy consumption is defined as the algebraic sum of energy consumed by each IEEE 802.15.4/ZigBee sensor mote during transmission, reception, and sleep modes. For simulation purpose, we consider the unit of energy consumption in mWh.
E t o t a l = E t r a n s m i t + E r e c e i v e + E s l e e p (7)
local byte hit rate increases as the size of the cache increases because more data can be cached locally for larger cache storage. According to the simulation results, the grid connection network has better byte hit ratio than the star / cluster connection network.
nearby cache node or the local hit may have occurred, and the data item is retrieved from the requesting node itself. For the Star/Cluster network with DEAR routing protocol, the average node energy consumption is 2.27 mWh. However, when DEAR routing protocol and CCZ cache algorithm are used together, the average energy consumption is 1.77 mWh.
Comparison of DEAR_CCZ with AODV_CCZ
a) For Star/Cluster-based Network
When AODV routing protocol is simulated with the localized CCZ caching algorithm then the average energy consumption was 2.43 mWh for cluster network [
b) For Grid/Peer-to-Peer based Network
When AODV routing protocol is simulated with the localized CCZ caching algorithm then the average energy consumption was 1.92 mWh for grid network [
Energy consumption by the nodes in the wireless environment is the cause of concern and minimization of Energy Consumption is the main crux of the proposed algorithm. The Simulation-based performance study was conducted to evaluate the proposed DEAR protocol with and without the use of localized caching algorithm; wherein the results show that the proposed scheme with the localized CCZ algorithm ensures that a query is served from the nearest cache or source for the network based on IEEE 802.15.4. Furthermore, the CCZ caching scheme performs significantly better in grid-based or peer-to-peer network model than the cluster based or star network model for DEAR routing protocol in terms of Byte Hit Ratio, Energy Consumption and the Average Query Latency. However, the future work would include a broader performance assessment for large ubiquitous network. We also intend to extend our scenario to a large heterogeneous wireless environment; wherein the devices of different wireless workgroup would be a part of the same network.
We would like to thank Integral Information and Research Centre of Integral University, Lucknow, India for providing an opportunity to carry out this research work. This work is an intellectual property of Integral University vides the Manuscript Communication No. IU/R&D/2019-MCN000600.
The authors declare no conflicts of interest regarding the publication of this paper.
Charan, P., Usmani, T., Paulus, R. and Saeed, S.H. (2019) Performance of Distributed Energy Aware Routing (DEAR) Protocol with Cooperative Caching for Wireless Sensor Networks. Wireless Sensor Network, 11, 35-45. https://doi.org/10.4236/wsn.2019.113003