Satisfy Quality Of Service Requirements Research Articles

Context‐aware application scheduling in fog computing environment

AbstractFog computing emerges as the new computing environment that stays in the proximity of end‐users and harnesses resources at the edge of the network to extend cloud‐facilities. It provides attractive solutions to the diverse range of Internet of Things (IoT) applications by executing them in the vicinity of end‐users. It is challenging to schedule these latency‐sensitive, computation‐intensive, and resource‐hungry applications on distributed, heterogeneous, and resource‐constrained Fog computing environment while ensuring time‐bound service delivery and satisfying Quality of Service (QoS) requirements of end‐users. In this paper, a context‐aware application scheduling technique is proposed for Fog computing environments that employs various parameters of device‐ and application‐level context to minimize service delivery time and satisfy QoS requirements of various IoT applications such as surveillance and game‐based applications. The performance of the proposed technique is evaluated in a simulated Fog environment and compared with baseline application scheduling techniques. The simulation results demonstrate that the proposed context‐aware scheduling techniques result in significant improvement in service delivery time and QoS compared to baseline scheduling techniques.

Backhaul Capacity-Limited Joint User Association and Power Allocation Scheme in Ultra-Dense Millimeter-Wave Networks

Millimeter-wave (mmWave) communication is considered a promising technology for fifth-generation (5G) wireless communications systems since it can greatly improve system throughput. Unfortunately, because of extremely high frequency, mmWave transmission suffers from the signal blocking problem, which leads to the deterioration of transmission performance. In this paper, we solve this problem by the combination of ultra-dense network (UDN) and user-centric virtual cell architecture. The deployment of dense small base stations (SBSs) in UDN can reduce transmission distance of signals. The user-centric virtual cell architecture mitigates and exploits interference to improve throughput by using coordinated multipoint (CoMP) transmission technology. Nonetheless, the backhaul burden is heavy and interbeam interference still severe. Therefore, we propose a novel iterative backhaul capacity-limited joint user association and power allocation (JUAPA) scheme in ultra-dense mmWave networks under user-centric virtual cell architecture. To mitigate interference and satisfy quality of service (QoS) requirements of users, a nonconvex system throughput optimization problem is formulated. To solve this intractable optimization problem, we divide it into two alternating optimization subproblems, i.e., user association and power allocation. During each iteration, a many-to-many matching algorithm is designed to solve user association. Subsequently, we perform power allocation optimization using a successive convex approximation (SCA) algorithm. The results confirm that the performance of the proposed scheme is close to that of the exhaustive searching scheme, which greatly reduces complexity, and clearly superior to that of traditional schemes in improving system throughput and satisfying QoS requirements.

QoS Support Path Selection for Inter-Domain Flows Using Effective Delay and Directed Acyclic Graph in Multi-Domain SDN

Currently, network applications, such as audio, video, and augmented reality, have different stringent service requirements. They require service provision through end-to-end connections via other networks with different operating environments or service conditions. Therefore, network operators require information on their own and other networks to provide end-to-end services traversing several networks while guaranteeing their quality of service (QoS) requirements. This study proposes an inter-domain flow decision method that satisfies QoS requirements using a directed acyclic graph (DAG) in multi-domain and hierarchical software defined networking (SDN) networks. There are multiple local networks with SDN controllers that are connected to the global SDN controller. The flow decision in the proposed method is based on the effective bandwidth theory of the martingale process. The effectiveness of the proposed method is demonstrated by comparing it with existing SDN-based path selection methods using the Riverbed Modeler (older, OPNET) and OpenDaylight SDN controllers.

Machine learning-based radio access technology selection in the Internet of moving things

The Internet of Moving Things (IoMT) takes a step further with respect to traditional static IoT deployments. In this line, the integration of new eco-friendly mobility devices such as scooters or bicycles within the Cooperative-Intelligent Transportation Systems (C-ITS) and smart city ecosystems is crucial to provide novel services. To this end, a range of communication technologies is available, such as cellular, vehicular WiFi or Low-Power Wide-Area Network (LPWAN); however, none of them can fully cover energy consumption and Quality of Service (QoS) requirements. Thus, we propose a Decision Support System (DSS), based on supervised Machine Learning (ML) classification, for selecting the most adequate transmission interface to send a certain message in a multi-Radio Access Technology (RAT) set up. Different ML algorithms have been explored taking into account computing and energy constraints of IoMT enddevices and traffic type. Besides, a real implementation of a decision tree-based DSS for micro-controller units is presented and evaluated. The attained results demonstrate the validity of the proposal, saving energy in communication tasks as well as satisfying QoS requirements of certain urgent messages. The footprint of the real implementation on an Arduino Uno is 444 bytes and it can be executed in around 50 µs.

Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models

Numerous middleware application programming interfaces (APIs) and protocols were introduced in the literature in order to facilitate the application development of the Internet of Things (IoT). Such applications are built on reliable or even unreliable protocols that may implement different quality-of-service (QoS) delivery modes. The exploitation of these protocols, APIs and QoS modes, can satisfy QoS requirements in critical IoT applications (e.g., emergency response operations). To study QoS in IoT applications, it is essential to leverage a performance analysis methodology. Queueing-network models offer a modeling and analysis framework that can be adopted for the IoT interactions of QoS representation through either analytical or simulation models. In this paper, various types of queueing models are presented that can be used for the representation of various QoS settings of IoT interactions. In particular, we propose queueing models to represent message-drop probabilities, intermittent mobile connectivity, message availability or validity, the prioritization of important information, and the processing or transmission of messages. Our simulation models demonstrate the significant effect on delivery success rates and response times when QoS settings are varied.

Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning

In this paper, we investigate a radio access network (RAN) slicing problem for Internet of vehicles (IoV) services with different quality of service (QoS) requirements, in which multiple logically-isolated slices are constructed on a common roadside network infrastructure. A dynamic RAN slicing framework is presented to dynamically allocate radio spectrum and computing resource, and distribute computation workloads for the slices. To obtain an optimal RAN slicing policy for accommodating the spatial-temporal dynamics of vehicle traffic density, we first formulate a constrained RAN slicing problem with the objective to minimize long-term system cost. This problem cannot be directly solved by traditional reinforcement learning (RL) algorithms due to complicated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coupled constraints</i> among decisions. Therefore, we decouple the problem into a resource allocation subproblem and a workload distribution subproblem, and propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">two-layer constrained</i> RL algorithm, named <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> esource <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> llocation and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</u> orkload di <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> tribution (RAWS) to solve them. Specifically, an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">outer layer</i> first makes the resource allocation decision via an RL algorithm, and then an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inner layer</i> makes the workload distribution decision via an optimization subroutine. Extensive trace-driven simulations show that the RAWS effectively reduces the system cost while satisfying QoS requirements with a high probability, as compared with benchmarks.

Cellular-Assisted Device-to-Device Communications for Healthcare Monitoring Wireless Body Area Networks

We envisage each person will wear an individual wireless body area network (WBAN) for healthcare monitoring. Two WBANs in proximity may collide. Such WBAN collisions can significantly degrade communication quality of service (QoS), in terms of throughput and latency of healthcare applications. To avoid collisions, we propose to use cellular-assisted device-to-device (D2D) communications to connect on-body sensors to a hub within WBAN. These D2D transmissions are performed concurrently with cellular transmissions and thus, interference is imposed on each other. A radio resource allocation scheme has been developed to manage the interference. The scheme uses a combination of difference of convex optimization and Hungarian algorithm to control transmit power and to assign communication channel to both cellular and D2D transmissions. The objective is to maximize the sum of cellular throughput while avoiding collisions among co-located WBANs and satisfying QoS requirements of healthcare applications. Evaluation results confirm that WBAN collisions can be avoided at a crowded pedestrian walkway, even when there are as many as 6 pedestrian arrivals per second.

Latency and Mobility–Aware Service Function Chain Placement in 5G Networks

5G networks are expected to support numerous novel services and applications with versatile quality of service (QoS) requirements such as high data rates and low end-to-end (E2E) latency. It is widely agreed that E2E latency can be reduced by moving the computational capability closer to the network edge. The limited amount of computational resources of the edge nodes, however, poses the challenge of efficiently utilizing these resources while, at the same time, satisfying QoS requirements. In this work, we employ mixed-integer linear programming (MILP) techniques to formulate and solve a joint user association, service function chain (SFC) placement, where SFCs are composed of virtualized service functions (VSFs), and resource allocation problem in 5G networks composed of decentralized units (DUs), centralized units (CUs), and a core network (5GC). Specifically, we compare four approaches to solving the problem. The first two approaches minimize, respectively, the E2E latency experienced by users and the service provisioning cost. The other two instead aim at minimizing VSF migrations along with their impact on users&#x2019; quality of experience with the last one minimizing also the number of inter-CU handovers. We then propose a heuristic to address the scalability issue of the MILP-based solutions. Simulations results demonstrate the effectiveness of the proposed heuristic algorithm.

An Adaptive and Fuzzy Resource Management Approach in Cloud Computing

Resource management plays a key role in the cloud-computing environment in which applications face with dynamically changing workloads. However, such dynamic and unpredictable workloads can lead to performance degradation of applications, especially when demands for resources are increased. To meet Quality of Service (QoS) requirements based on Service Level Agreements (SLA), resource management strategies must be taken into account. The question addressed in this research includes how to reduce the number of SLA violations based on the optimization of resources allocated to users applying an autonomous control cycle and a fuzzy knowledge management system. In this paper, an adaptive and fuzzy resource management framework (AFRM) is proposed in which the last resource values of each virtual machine are gathered through the environment sensors and are sent to a fuzzy controller. Then, AFRM analyzes the received information to make decision on how to reallocate the resources in each iteration of a self-adaptive control cycle. All the membership functions and rules are dynamically updated based on workload changes to satisfy QoS requirements. Two sets of experiments were conducted on the storage resource to examine AFRM in comparison to rule-based and static-fuzzy approaches in terms of RAE, utility, number of SLA violations, and cost applying HIGH, MEDIUM, MEDIUM-HIGH, and LOW workloads. The results reveal that AFRM outweighs the rule-based and static-fuzzy approaches from several aspects.

Utility-Based Opportunistic Scheduling Under Multi-Connectivity With Limited Backhaul Capacity

Multi-connectivity is a 5G key enabler to fulfill multi-service quality-of-service (QoS) requirements. This letter examines an opportunistic resource allocation problem under multi-connectivity and limited backhaul capacity in evolved LTE using two utility functions: 1) proportional fairness (PF) and 2) utility proportional fairness (UPF). We then propose an efficient algorithm to deal with the formulated NP-hard problem. Such algorithm guarantees to produce a solution with an explicit bound on the optimal solution. Finally, simulation results justify that UPF utility function with opportunistic scheduling in multi-connectivity can better satisfy QoS requirements.

Hierarchical Edge-Cloud SDN Controller System With Optimal Adaptive Resource Allocation for Load-Balancing

Although the openflow-based software defined network (SDN) architecture can ease the workload of network control and management and separate it from the switch/routing operations, a computation-resource limited controller can still be congested by heavy flows and then experiences serious delay. To enhance network scalability and reduce computation delay on SDN networks under Quality of Service (QoS) requirements, a hierarchical edge-cloud SDN (HECSDN) controller system design is proposed with three features. First, by sharing computational resources in the edge and the cloud, the system architecture provides a flexible mechanism for devices to allocate their computational tasks according to traffic loads. The second feature is to design a queueing model of the proposed architecture. The model description of the networking architecture enables the network designers to quickly estimate the performance of design without considerable time and cost in experimental setups. Third, derived from the queueing model, an efficient load-balancing algorithm satisfying QoS requirements or fairness allocation for different applications in the HECSDN architecture is proposed. This newly-developed multi-tier controller system has been proved to be effective even when working on a large-scale SDN, without sacrificing the overall performance. Moreover, this system stays highly stable in transient periods even under highly fluctuating flow arrivals.

LLTP-QoS: Low Latency Traffic Prioritization and QoS-Aware Routing in Wireless Body Sensor Networks

Wireless Body Sensor Network (WBSN) is deployed in delay-sensitive application scenarios where providing Quality-of-Service (QoS) is utmost important. The QoS-aware routing protocol not only discovers a route from source to destination but also satisfies QoS requirements in heavily loaded wireless networks. Emergency/critical data packets must reach the intended destination without incurring significant delays and fulfill multiconstrained demands (reliability, delay, Packet Delivery Ratio (PDR)) of heterogeneous applications. Congestion occurs in heavy traffic situation when the incoming traffic load exceeds the capacity of transmission link, buffer overflows, packet collision, channel contention. Consequently, it impacts QoS in terms of packet loss, end-to-end delay and PDR. Moreover, the selection of poor links/routes may have detrimental impacts of the performance of WBSN and there can be significant variations in throughput, delay, network lifetime, route stability performance. Majority of the existing priority-aware routing protocols proposed for Medium Access Control (MAC) layer to solve the slot allocation problem by which data packets are classified into different categories. However, less attention has been given to traffic prioritization at network layer for data categorization. Furthermore, optimized traffic prioritization has been overlooked, thereby increases the data redundancy, queue/link delay, data loss and decreases the reliability of the network, and it does not satisfy the QoS requirements of WBSN and affects the critical data to be delivered in a less privileged manner. This work proposes the Low Latency Traffic Prioritization scheme for QoS-aware routing (LLTP-QoS). The LLTP-QoS is designed to enhance the transmission of critical data in a privileged manner (reliability) and avoids the end-to-end delay. The performance of proposed scheme is evaluated in terms of throughput, average end-to-end delay, PDR, normalized routing load, network lifetime through extensive simulations using Network Simulator-2 (NS2). The simulation results verified improved performance of proposed LLTP-QoS scheme as compared to existing routing protocols.

Handover optimization scheme for LTE-Advance networks based on AHP-TOPSIS and Q-learning

With the emblematical expansion of telecommunication networks, high data rate, and low latency for User Equipment (UE) has arisen. 3GPP introduced Long Term Evolution (LTE), LTE-Advance (A) in release 8 and release 10, respectively. These technologies support different types of traffic in a network such as a video, voice, data, etc. It also creates new challenges for resource management and seamless connectivity when UEs are mobile. Handover facilitates transfer the control of UE from serving evolve Node (eNB) to target eNB without any interruption. During mobility of UEs, if the triggering time and selection of eNB out of available eNBs is not done optimally for handover, then Quality of Service (QoS) requirements may be breached. Hence, for seamless connectivity, selection of eNB and triggering points needs to be further optimized to satisfy QoS requirements. In this paper, an intelligent scheme based on AHP-TOPSIS method and Q-learning approach is proposed for handover optimization. The performance of the network with the proposed scheme is analyzed numerically and with the help of simulation also. Results show that the proposed scheme minimizes the Handover Failure Rate (HFR) and Handover Ping-Pong (HPP), effectively to 28%, 25% and 35%, 33% as compared to conventional method and Fuzzy Multiple-Criteria Cell Selection (FMCCS) scheme.

QoS-data rate optimization for fast fading interference alignment X network

Quality of service (QoS) and high data rate are two main parameters that drive the research in wireless communication. Perfect Channel State Information (CSI) at both transmitter and receiver sides is an important condition for achieving QoS and high data rate. But, achieving perfect CSI in mobile transmitters/receivers under fast fading is challenging. That is, frequent CSI updating is required to satisfy QoS requirements under fast fading scenario.In this paper, we consider a multiple transmitter-receiver system, called X network, that has data flow in all the channels. Interference alignment (IA) is a practical solution to achieve maximum capacity in an X network. The present contribution explores the performance of the system with a mobile receiver (or transmitter) that employs CSI estimation using pilot symbols, and further optimize the performance so as to cater both bit error rate (BER) and data rate requirements. Using Monte-Carlo simulations, a novel algorithm is proposed to calculate optimal pilot overhead in M×2 X network or 2×M X network by setting an upper bound on BER. With simulation results we show that the optimal overhead varies with Doppler frequency and Signal-to-Noise Ratio (SNR).

Multiband Spectrum Sensing and Resource Allocation for IoT in Cognitive 5G Networks

The proliferation of the Internet of Things (IoT) demands a diverse and wide range of requirements in terms of latency, reliability, energy efficiency, etc. Future IoT systems must have the ability to deal with the challenging requirements of both users and applications. Cognitive fifth generation (5G) network is envisioned to play a key role in leveraging the performance of IoT systems. IoT systems in cognitive 5G network are expected to provide flexible delivery of broad services and robust operations under highly dynamic conditions. In this paper, we present multiband cooperative spectrum sensing and resource allocation framework for IoT in cognitive 5G networks. Multiband approach can significantly reduce energy consumption for spectrum sensing compared to the traditional single-band scheme. We formulate an optimization problem to determine a minimum number of channels to be sensed by each IoT node in multiband approach to minimize the energy consumption for spectrum sensing while satisfying probabilities of detection and false alarm requirements. We then propose a cross-layer reconfiguration scheme (CLRS) for dynamic resource allocation in IoT applications with different quality-of-service (QoS) requirements including data rate, latency, reliability, economic price, and environment cost. The potential game is employed for cross-layer reconfiguration, in which IoT nodes are considered as the players. The proposed CLRS efficiently allocate resources to satisfy QoS requirements through opportunistic spectrum access. Finally, extensive simulation results are presented to demonstrate the benefits offered by the proposed framework for IoT systems.

QoS Provisionings for Device-to-Device Content Delivery in Cellular Networks

Device-to-device (D2D) technique provides a feasible way for content delivery among proximal users without deploying additional infrastructures. For the realization of D2D content delivery, it is important to guarantee quality-of-service (QoS) for supporting real-time content transmissions. In this paper, we formulate the content delivery problem based on a flow model and investigate mechanisms providing delay QoS guarantee. The delay performance is analyzed for the content dissemination with the aid of D2D communication. Based on the analyses of parameters affecting the performance of content delivery, an optimal resource allocation scheme is proposed to achieve the best energy efficiency of content delivery in cellular networks with guaranteed delay constraints for hybrid content requests. Then effects of content caching on D2D content delivery is studied and a caching management scheme is proposed to avoid cache overflow of users with a certain probability and satisfy QoS requirements of content delivery. Simulation results are presented that verify the analyzed influencing factors on D2D content delivery and the proposed QoS-provisioning schemes.

Survey on Monitoring and Quality Controlling of the Mobile Biosignal Delivery.

A Mobile Patient Monitoring System (MPMS) acquires patient's biosignals and transmits them using wireless network connection to the decision-making module or healthcare professional for the assessment of patient's condition. A variety of wireless network technologies such as wireless personal area networks (e.g., Bluetooth), mobile ad-hoc networks (MANET), and infrastructure-based networks (e.g., WLAN and cellular networks) are in practice for biosignals delivery. The wireless network quality-of-service (QoS) requirements of biosignals delivery are mainly specified in terms of required bandwidth, acceptable delay, and tolerable error rate. An important research challenge in the MPMS is how to satisfy QoS requirements of biosignals delivery in the environment characterized by patient mobility, deployment of multiple wireless network technologies, and variable QoS characteristics of the wireless networks. QoS requirements are mainly application specific, while available QoS is largely dependent on QoS provided by wireless network in use. QoS provisioning refers to providing support for improving QoS experience of networked applications. In resource poor conditions, application adaptation may also be required to make maximum use of available wireless network QoS. This survey paper presents a survey of recent developments in the area of QoS provisioning for MPMS. In particular, our contributions are as follows: (1) overview of wireless networks and network QoS requirements of biosignals delivery; (2) survey of wireless networks' QoS performance evaluation for the transmission of biosignals; and (3) survey of QoS provisioning mechanisms for biosignals delivery in MPMS. We also propose integrating end-to-end QoS monitoring and QoS provisioning strategies in a mobile patient monitoring system infrastructure to support optimal delivery of biosignals to the healthcare professionals.

Uplink Scheduling and Power Allocation for M2M Communications in SC-FDMA-Based LTE-A Networks With QoS Guarantees

Providing diverse and strict quality-of-service (QoS) guarantees is one of the most important requirements in machine-to-machine (M2M) communications, which is particularly need for appropriate resource allocation for a large number of M2M devices. To efficiently allocate resource blocks (RBs) for M2M devices while satisfying QoS requirements, we propose group-based M2M communications, in which M2M devices are clustered based on their wireless transmission protocols, their QoS characteristics, and their requirements. To perform joint RB and power allocation in SC-FDMA-based LTE-A networks, we formulate a sum-throughput maximization problem, while respecting all the constraints associated with SC-FDMA scheme, as well as QoS requirements in M2M devices. The constraints in uplink SC-FDMA air interface in LTE-A networks complicate the resource allocation problem. We solve the resource allocation problem by first transforming it into a binary integer programming problem and then formulate a dual problem using the Lagrange duality theory. Numerical results show that the proposed algorithm outperforms traditional Greedy algorithm in terms of throughput maximization while satisfying QoS requirements, and its performance is close to the optimal design.

Optimized Low Complexity Service Based onReliable Routing Algorithm using PSO for Vehicular Ad hoc Networks (VANETs)

Objectives: To explored the highlighted obliged QoS routing issue in Vehicular Ad hoc Networks (VANETs). Methods/ Statistical Analysis: The issues are tended to firstly by working up an association faithful quality model considering the topological and numerical properties of vehicular advancements and speeds. The VANET correspondence diagram in advancing chart hypothesis is used to model and organize the formed association trustworthiness shown into it. In light of the consequent intensified chart diagram illustrate, the most tried and true course in the framework is picked. However course assurance does not ensure awesome transmission as well. Findings: Vehicular extraordinarily designated Systems (VANETs) are a particular sort of remote framework made by vehicles giving among themselves and with roadside base stations. A broad assortment of organizations has been made for VANETs going from security to infotainment applications. A key need for such organizations is that they are offered with Nature of Administration Quality of Service (QoS) guarantees similar to organization unfaltering quality and availability. QoS routing expect an essential part in recognizing courses that meet the QoS necessities of the offered organization over VANETs. For filtering the achievable courses it subject to various QoS goals is with everything taken into account a NP-difficult issue. Furthermore, the reliability of the routing ought to be given unprecedented thought as correspondence associations, as frequently as conceivable in VANETs. In present situation, the most existing QoS routing calculations are been PSO algorithm for stable VANET system without considering the security and wellbeing of the routing procedure. Along these lines, they are not appropriate for applications in VANETs. Application/Improvements: Low complexity nature advanced administration dependable routing calculation is made to ensure to lessen transmission redundancy with use of Subterranean insect State estimation with better eventual outcomes of course decision and transmission considering better request and sorting calculations in the Particle Swam Optimization (PSO) algorithm work calculation for extremely powerful VANET system with less preferred quality in evaluation with favoured data transmission over interchange QoS counts as shown by the results and satisfy QoS requirements of specific traffic using PSO in the VANET routing protocol environments. Meanwhile, an optimized function with PSO is also proposed to make scheduling decisions so that the dropped packet rates are reduced. The results of PSO algorithm simulations are better and feasible as compared to the previous techniques.

An Efficient Next Hop Selection Algorithm for Multi-Hop Body Area Networks.

Body Area Networks (BANs) consist of various sensors which gather patient’s vital signs and deliver them to doctors. One of the most significant challenges faced, is the design of an energy-efficient next hop selection algorithm to satisfy Quality of Service (QoS) requirements for different healthcare applications. In this paper, a novel efficient next hop selection algorithm is proposed in multi-hop BANs. This algorithm uses the minimum hop count and a link cost function jointly in each node to choose the best next hop node. The link cost function includes the residual energy, free buffer size, and the link reliability of the neighboring nodes, which is used to balance the energy consumption and to satisfy QoS requirements in terms of end to end delay and reliability. Extensive simulation experiments were performed to evaluate the efficiency of the proposed algorithm using the NS-2 simulator. Simulation results show that our proposed algorithm provides significant improvement in terms of energy consumption, number of packets forwarded, end to end delay and packet delivery ratio compared to the existing routing protocol.