In light of the revolutionary requirements of the sixth generation (6G) and beyond wireless networks, reconfigurable intelligent surface (RIS) and rate-splitting multiple access (RSMA) have emerged as pivotal technologies due to their potential for improving spectral efficiency, user fairness, and interference management. This survey explores the theoretical foundations, architectural frameworks, and design strategies of RIS-assisted RSMA, emphasizing the combined adaptability of RIS’s wireless propagation control and RSMA’s multi-user flexibility for dynamic spectrum management. The article first discusses the fundamental concepts of RSMA and RIS technologies. Then, we investigate various enabling technologies for RIS-RSMA networks, highlighting key advancements in interference mitigation, energy efficiency, and security for future networks. Subsequently, some optimization techniques crucial for enhancing RIS-RSMA network performance are presented. Additionally, we examine advanced machine learning (ML) approaches that enable RIS configurations to dynamically adapt to changing network requirements. Techniques such as deep reinforcement learning support real-time adjustments, creating more scalable and resilient RIS-RSMA architectures. Finally, we discuss open research directions for advancing RIS-assisted RSMA in emerging 6G applications. We also consider the potential of advanced ML techniques, including quantum-based ML and large language models, to handle the complexities of large-scale network optimization. This comprehensive survey addresses critical challenges and current advancements. It offers a roadmap for future research in RIS-assisted RSMA networks, paving the way for robust, intelligent, and adaptive 6G wireless communication systems.

Data collection techniques can be used to determine the coverage conditions of a cellular communication network within a given area. In such tasks, the data acquisition process faces significant challenges for larger or inaccessible locations. Such challenges can be alleviated through the use of unmanned aerial vehicles (UAVs). This way, data acquisition obstacles can be overcome to acquire and process the necessary data points with relative ease to estimate a full area coverage map for the concerned network. In this study, we formulate the problem of deploying a UAV to acquire the minimum possible measurement data points in a geographical region for the purpose of constructing a full communication coverage gap map for this region. We then devise an estimation model that utilizes the measured data samples and determines the noise/loss levels of the communication links at the other unvisited spots of the region accordingly. The proposed estimation model is based on a cascade-forward neural network to allow for both nonlinear and direct linear relationships between the input data and the output estimations. We further investigate the conventional method of using linear regression estimators to decide on the received power levels at the different locations of the examined area. Our simulation evaluations show that the proposed nonlinear estimator outperforms the conventional linear regression technique in terms of the communication coverage error level while using the minimum possible collected data points. These minimum data points are then used in constructing a complete coverage gap map visualization that demonstrates the overall network service conditions within the surveyed region.

Balancing traffic among cellular networks is very challenging due to many factors. Nevertheless, the explosive growth of mobile data traffic necessitates addressing this problem. Due to the problem complexity, data-driven self-optimized load balancing techniques are leading contenders. In this work, we propose a comprehensive deep reinforcement learning (RL) framework for steering the cell individual offset (CIO) as a means for mobility load management. The state of the LTE network is represented via a subset of key performance indicators (KPIs), all of which are readily available to network operators. We provide a diverse set of reward functions to satisfy the operators' needs. For a small number of cells, we propose using a deep Q-learning technique. We then introduce various enhancements to the vanilla deep Q-learning to reduce bias and generalization errors. Next, we propose the use of actor-critic RL methods, including Deep Deterministic Policy Gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3) schemes, for optimizing CIOs for a large number of cells. We provide extensive simulation results to assess the efficacy of our methods. Our results show substantial improvements in terms of downlink throughput and non-blocked users at the expense of negligible channel quality degradation.

Drones are expected to facilitate extending wireless networks’ access for both human users and smart machine-type-communication devices (MTCDs) with strict and diverse quality of service (QoS) requirements. In this paper, we propose an optimal solution for the dynamic placement of an LTE drone-mounted base station to maximize the coverage of the MTCDs deployed over a large geographical area. The resulting technique jointly optimizes the drone's 3D positioning to maximize coverage and allocates the network resources in such a way that gives high priority to the delay-sensitive machine-to-machine (M2M) traffic. This optimization algorithm determines the optimal bound of the solution. Since it cannot be used in real-time operations due to its computational complexity, we also introduce a heuristic technique that offers a near-optimal solution with much reduced complexity. We conduct several simulation evaluations to assess the proposed techniques. These results are compared to those of other drone placement approaches. The comparisons show that the proposed techniques offer significantly better results in the communication coverage while fulfilling the diverse QoS requirements of the deployed M2M network. Moreover, the heuristic-based technique is shown to succeed in finding solutions close to the optimal bound with a considerable reduction of complexity over the exact algorithm.

Unmanned aerial vehicles (UAVs) are increasingly becoming an integral part in many civilian and military applications. One of the main requirements for the UAV in such applications is the ability of the UAV to determine its location in an autonomous and real time manner. While many applications rely on the GPS system for this purpose, existing GPS based localization methods face many challenges and do not provide a highly reliable and accurate 3D positioning solution. The objective of this paper is to provide an accurate and real-time solution for the 3D localization of UAVs in an outdoor environment using existing 5G networks, independent of the GPS. We formulate the UAV localization as an optimization problem in which the drone uses the RSSI measurements of the surrounding 5G base stations, without having to actually interact with these base stations, to determine its location. We solve the formulated localization problem using a proper optimization technique to determine the optimal bound of the solution. Then, we propose a deep supervised learning approach to provide a localization solution with comparable accuracy for practical real-time dynamic applications.

Scheduling in 5G networks is a challenging task due to the heterogeneous Quality of Service (QoS) requirements of traffic sources. In this paper, we consider the problem of uplink scheduling in 5G networks for mixed traffic that includes Ultra-Reliable Low Latency Communications (URLLC) devices and enhanced Mobile Broad-Band (eMBB) users. For this purpose, a mathematical model for Grant Free (GF) services is derived for the k-repetitions Hybrid Automatic Repeat reQuest (HARQ). We formulate the scheduling problem as a mixed-integer non-linear programming optimization problem. We introduce a complete system model that includes grant-free and grant-based subsystems. We then introduce our proposed solution to the scheduling problem that addresses the two traffic types. Different scheduling techniques are then compared and a performance upper bound is added as a reference. The results show that the proposed technique provides near-optimal results and outperforms other scheduling techniques with a significant complexity reduction.

In this paper, we propose a machine learning-based indoor/outdoor (IO) user classification algorithm in cellular systems as pertains to 3G networks. We consider different scenarios. The experimental results show that the best machine learning algorithm for IO classification is the boosting algorithm with an accuracy that reaches 88.9%.

In this paper, we consider the problem of energy optimization in mobile networks by enabling the MIMO feature only when necessary. Enabling MIMO features at the base station increases energy consumption unnecessarily under many operating conditions. In this study, we employ machine learning-based approaches to decide on whether a SISO scheme can achieve the required Quality of Experience (QoE). If SISO can satisfy the target QoE, the base-station can decide to switch the MIMO feature off which can result in considerable energy savings. We consider two different machine learning approaches, namely, multi-layer perceptron (MLP) and recurrent neural networks (RNNs), to learn the SISO features from realistic mobile network data. The trained models are tested against the data obtained from MIMO cells in which the MIMO feature is disabled. Our results show the effectiveness of our proposed approach which presents a real-time, automated approach for MIMO enabling decisions.

Balancing traffic among network installed radio base stations is one of the main challenges facing mobile operators because of the unhomogeneous geographical distribution of mobile subscribers in addition to practical and environmental limitations preventing acquiring the best locations to build radio sites. This increases the challenge of satisfying the increasing data speed demand for smartphone users. In this paper, we present a reinforcement learning framework for optimizing neighbor cell relational parameters that can better balance the traffic between different cells within a defined geographical cluster. We present a comprehensive design of the learning framework that includes key system performance indicators and the design of a general reward function. System level simulations show that reinforcement learning based optimization for neighbor cell borders can significantly improve overall system performance; in particular, with a reward function defined as throughput, an improvement up to 50% is achieved.

Ultra-reliable low-latency communication (URLLC) has been considered as a major use case for the fifth generation (5G) wireless networks. Therefore, the Third Generation Partnership Project (3GPP) has targeted the support of URLLC in the new radio (NR) air interface by introducing several technologies such as short transmission time intervals (sTTIs) and puncturing scheduling. However, scheduling URLLC without impacting the quality-of-service (QoS) of enhanced mobile broadband (eMBB) traffic is a challenging task. In this paper, we optimize the resource allocation and scheduling process of URLLC puncturing eMBB transmissions by considering the QoS of eMBB and the transmission errors associated with the finite blocklength coding of the URLLC traffic. In addition, we propose a deep supervised learning approach to predict the optimized resource allocation in a computationally efficient manner to be practically used in real-time operation. The numerical results show that by adjusting the model parameters, we can increase the accuracy of the lowcomplexity predictions for an efficient scheduling scheme with superior performance as compared to other techniques.

Shortened transmission time interval (sTTI) is considered one of the most significant enhancements to evolve the current Long-Term Evolution (LTE) networks to fulfill the 5G requirement of supporting critical machine-type communications (cMTC). However, scheduling the sporadic cMTC and the data-intensive human-type communications (HTC) on different scales of transmission duration is a challenging task. In addition, the finite blocklength coding of the small-size packets of cMTC makes the process more challenging. In this paper, we address the scheduling problem of cMTC coexistent with HTC in LTE. In this regard, we formulate the processes as optimization problems that maximize the system utility while providing guarantees for the different quality-of-service (QoS) of both types of traffic. Moreover, we propose computationally-efficient algorithms that utilize the matching theory and analyze them from a practical perspective to prove that they can be used as practical schemes. The simulation results show a close-to-optimal performance for the proposed scheme and its superiority to conventional algorithms.

The deployment of drone-mounted communication systems has received increasing interest and attention recently as it allows significant improvement to the network access capacity and coverage. Many applications can benefit from such deployments in particular machine-to-machine (M2M) communications. In this study, we propose a technique for the rapid deployment of an LTE drone-mounted base station to serve a group of machine-type-communication devices (MTCDs) that are deployed in disaster situations or within remote applications. The objective is to maximize the number of served MTCDs while meeting their transmission delay requirements. This results in optimizing the 3D placement of the drone base station as well as the allocation of the available communication resources. We evaluated the proposed technique through several simulation experiments. The simulation results demonstrate significant improvement in the aggregate system throughput and the overall system deadline missing performance along with the increased number of served MTCDs compared to other drone placement approaches.

Cellular systems present one of the most suitable wireless technologies to efficiently serve critical machine‐type communications (MTC) that require strict quality‐of‐service (QoS) guarantees. Therefore, ultra‐reliable and low‐latency communications is a target use case in the design of the upcoming generations of cellular networks. From the radio resource management perspective, guaranteeing such stringent QoS requirements in long term evolution (LTE) networks is a challenging task, especially in the case of the coexistence of MTC with the human‐type communications (HTC). In this study, the authors address the resource allocation and scheduling problem of critical MTC that coexist with HTC in LTE. The optimisation problem is formulated such that the overall system utility is maximised while fulfilling the different QoS demands of the two sets of users. Utilising the effective bandwidth and effective capacity theories, a cross‐layer design is developed to guarantee the QoS requirements of the critical MTC. For a computationally‐efficient solution of the problem, they formulate it as a two‐sided matching process that can be used as a practical scheduling scheme. To this end, they analyse the convergence, stability, and computational complexity of the proposed methods. Results reveal the close‐to‐optimal performance of the matching‐based scheduling scheme and its superiority to other existing techniques.

The accelerated move toward the adoption of the Industrial Internet-of-Things (IIoT) paradigm has resulted in numerous shortcomings as far as security is concerned. One of the IIoT affecting critical security threats is what is termed as the false data injection (FDI) attack. The FDI attacks aim to mislead the industrial platforms by falsifying their sensor measurements. FDI attacks have successfully overcome the classical threat detection approaches. In this article, we present a novel method of FDI attack detection using autoencoders (AEs). We exploit the sensor data correlation in time and space, which in turn can help identify the falsified data. Moreover, the falsified data are cleaned using the denoising AEs (DAEs). Performance evaluation proves the success of our technique in detecting FDI attacks. It also significantly outperforms a support vector machine (SVM)-based approach used for the same purpose. The DAE data cleaning algorithm is also shown to be very effective in recovering clean data from corrupted (attacked) data.

The Internet of Things is envisioned to provide communication links to billions of Machine-Type Communication Devices (MTCDs). The Long Term Evolution (LTE) protocol is expected to be one of the main enablers of such connections. With the massive increase in data traffic, the Mobile Network Operators (MNOs) have resorted to enabling wireless resource sharing amongst virtual network operators in what is known as Network Virtualization (NV). Most resource sharing schemes have been adapted to the conventional Human-to- Human (H2H) communications. Since many MTCD deployments support mission-critical applications, the resource sharing scheme needs to conform to the delay-sensitive traffic requirements of such deployments. In this study, we propose a novel wireless resource slicing scheme that allows the virtual network operators to trade their allocated resources optimally. The performance of the proposed scheme is evaluated and compared to common network virtualization schemes from the literature. The new scheme has shown to achieve significantly better performance than these schemes from the traffic delay requirements perspective.

The Internet of Things (IoT) has become a major paradigm on which many applications are based. While it resulted in the introduction of numerous smart systems that were never thought possible until recently, it also posed challenges in terms of the deployment costs of many generally low-cost applications. One of these challenges is the resource and service sharing among nodes of deployed applications that belong to multiple owners and vendors. To address this issue, the node social relationships model is used to manage the communications and routing duties. We therefore propose a social relationship-based routing protocol that considers, as its main metric, the relationships of the relay nodes with the source node. We designed and implemented the routing protocol, which we term the Socially Weighted Shortest Path (SWSP) protocol. We verified the performance of the proposed technique in comparison to a common shortest path routing protocol.

One of the important topics that have been extensively studied in the Wireless Sensor Networks (WSNs) literature is Temporal Topology Control (TTC). TTC is used as a tool to manage the sleep/wake cycles of sensor nodes (SNs) in dense WSNs (i.e. WSNs characterized by a high level of SN redundancy), whether deployed in a planned or a random fashion. The primary objectives of existing TTC protocols (TTCPs) are maximizing WSN lifetime and minimizing packet collision in the network. However, due to their significant communication and processing overhead, existing TTCPs can be slow in reacting to potential SN failures and hence are not suitable for WSNs serving critical Internet of Things applications where the reliability of WSN operation is a major concern. In this paper, we propose a TTCP targeted for reliable WSN deployments. The proposed protocol is based on the assumption that the WSN deployment is composed of a number of disjoint connected-covers. We implement the proposed protocol using a network simulator and apply the proposed protocol on different deployment scenarios. We present and discuss the experimental results in terms of two protocol performance metrics: the incurred overhead and the time required to detect and repair the functionality of the WSN due to potential SN failures. The factors which affect these performance metrics are also highlighted.

Machine-type Communications (MTC) are expected to dominate cellular networks traffic by the end of this decade.This makes the radio resource allocation, i.e. scheduling, on these networks, a challenging task. The limited radio resources may not be sufficient for the data transmissions of all the MTC devices (MTCDs) especially in case of massive M2M deployments. Hence, it is essential to allocate radio resources to the MTCDs that send non-redundant or unique data since they are considered to have higher importance. In this paper, we introduce a novel Machine-to-Machine (M2M) resource allocation metric that we term the statistical priority. Statistical priority evaluates the importance of data sent by MTCDs. The importance of a data unit is quantified based on some statistical functions such as, comparison with upper and lower thresholds, difference with earlier data units, and detecting an increasing or decreasing trend when combined with previous data units for prioritizing the allocation of the scarce radio resources to MTCDs sending unique data. Performance evaluation shows that our proposed metric helps achieve effective resource utilization by letting MTCDs send a reduced set of their data that constitute the most important data units that can fully represent the full set of data units.

Machine-to-Machine (M2M) scheduling over Long Term Evolution (LTE) networks is an essential research area for future communications. This is due to the strong expectations that M2M communications will be a main element of the overall traffic over 5G networks. The diversity of M2M applications strongly motivates studying the problem of resource allocation in the uplink direction where the M2M traffic is dominant. M2M communications impose requirements that differ from those required by Human-to-Human (H2H) communications. We present a classification of M2M scheduling techniques from the perspective of these requirements. We then propose an M2M uplink scheduling algorithm that offers a balance between throughput and delay requirements. It is also adaptive to traffic characteristics since it considers both channel state and system deadlines in an adjustable manner according to network needs. Finally, we conduct experiments to compare the performance of the proposed technique to that of other schedulers that belong to the different M2M scheduler categories.

The Internet of Things (IoT) is generally based on mobile and stationary communication objects. These objects communicate the information that they collect to other potentially remote objects for processing. We view the IoT as composed of numerous intranets of Things. Each intranet of Things (ioT), which may belong to an organization or an enterprise, may contain several applications. Each of these applications monitors phenomena and collects data that could be of interest to a certain party. Nevertheless, ensuring that these potentially separate and different applications are managed uniformly is not an easy task for the ioT operators. We therefore need to establish an arrangement by which we facilitate the co-existence, cooperation and management of these applications. In this study, we introduce a unified framework to facilitate the management of existing mobile and static wireless sensor network (WSN) applications as well the introduction of new WSN applications for a given ioT. This framework includes network and application maintenance tasks as well as the tasks required to connect these applications to the IoT. We provide a solid example on how this framework can be implemented. Finally, we present some experimental results on the performance of this example implementation from different perspectives.

The Sensor Deployment Problem (SDP) is one of the most studied problems in the field of Wireless Sensor Networks (WSNs). It can generally be defined as selecting the sensors locations in a specified Region of Interest (RoI) to achieve one or more design objectives of the WSN. Two of the commonly required design objectives are maximizing coverage and minimizing the deployment cost of the WSN. In this paper, we address the SDP of covering a finite set of target locations in a specified RoI using non-homogenous, non-isotropic sensors with minimum sensor deployment cost. We propose a novel approach for solving the SDP using a Variable-Length Genetic Algorithm (VLGA). We apply our proposed algorithm on a WSN surveillance case-study to evaluate its performance. Based on the experimental results, we show that our proposed algorithm outperforms an existing approach which uses a Fixed-Length GA (FLGA) in terms of the quality of obtained solutions, speed of convergence and scalability.

Wireless sensor networks (WSNs) are considered one of the main building blocks of the Internet of Things (IoT). Therefore, connecting these networks to data recipients globally becomes a basic ingredient for the success of the entire IoT paradigm. We introduce a practical solution for connecting these networks to global data consumers via the existing Internet infrastructure. Our proposed solution takes into consideration the limited resources of the WSN nodes especially energy. It also stems from actual characteristics of practical deployments of these networks as well as the current and expected future state of the technology. We present the elements of our solution and discuss the different features which are offered by these elements. We then conduct an experimental evaluation which compares our solution to other existing solutions from the standpoint of network performance and the associated resource requirements that are imposed by these solutions.

In many wireless sensor network (WSN) applications, there are many practical considerations that need to be factored into the design of the underlying networking protocols. For example, the need to involve more than one sink node, i.e. more than one data destination, in the network could be one of the main requirements. This requirement stems from the need for the WSN to monitor several phenomena with potentially different interested parties for each monitored phenomenon. The synchronized operation of the WSN nodes is another important requirement in order to ensure that the actions taken as a result of a detected event are timely and accurate. Several routing protocols have been proposed for WSNs in the past. However, when these protocols are put in practical use, it was found their inability to provide the robust performance that is required for many sensitive applications. In this paper, we propose a new multi-sink routing protocol, which we call ECTP, which we target for the oil and gas industry. In such an application, the reliable synchronized data delivery to multiple destinations (sinks) is required. We perform several practical experiments using actual WSN hardware to evaluate the proposed protocol. We discuss the results of these experiments and propose future directions for this research.

Transmission collision is one of the main reasons of performance degradation in dense wireless sensor networks (WSNs). Transmission collision can cause throughput reduction, excessive delay and packet loss. One of the methods to minimize the probability of packet collision is the reduction of the collision area (area around sending nodes where collision may take place). In this study we investigate the problem of collision probability minimization through the use of cooperative transmissions and optimal power allocation in WSNs. We formulate the problem as a constrained optimization problem subject to an outage probability constraint. We determine the optimal transmission power of the source and the relay nodes which minimizes the collision area. Results show that the proposed technique significantly reduces the collision area while keeping the outage performance below the targeted value. Results also show that the proposed technique outperforms the direct transmission system as well as the cooperative system with equal transmission power.

The Internet of Things (IoT) aims to connect the growing number of sensing and monitoring devices to humans or other devices with the goal of exchanging data or controlling these devices. IoT can be considered as the set of technologies that allow small devices e.g. sensors, which constitute wireless sensor networks (WSNs) that perform specific tasks, to communicate over the Internet Protocol (IP). Cellular networks, especially LTE, are an attractive technology to provide Internet connectivity to these potentially remote devices. In order to allow the cellular networks to connect these devices reliably to the Internet, the traffic of WSNs should be studied to understand their unique requirements on the cellular infrastructure. This is because the existing cellular networks have been traditionally designed mainly to support human to human communication in which case the traffic is completely different than that of WSNs. This paper aims to shed some light on the possible integration challenges that are imposed by the integration of WSNs and LTE which manifest themselves in the difference in traffic characteristics. We perform some experiments to explore the behavior of specific WSN application traffic. We then discuss the possible adaptation that is required for the successfully integration of the two technologies.

ABSTRACT In this study, we develop a fully distributed routing protocol for OFDMA‐based multihop broadband wireless access (BWA) networks such as those of IEEE 802.16j. We refer to this protocol as the DCLRRA protocol. DCLRRA is based on autonomous resource allocation schemes that we also derive in this paper. The routing protocol's selection of the proper resource allocation scheme is based on whether the relay stations (RSs) are nomadic or stationary. While we develop the autonomous resource allocation schemes, we exploit the multi‐user capabilities of the OFDMA physical layer. This allows simultaneous data transmission sessions within the same neighborhood while offering a total elimination of interference between transmitting nodes. The direct result of this strategy is increased throughput with high utilization of the communication channel. We examine our routing technique to show its performance merits through extensive simulations. Copyright © 2010 John Wiley & Sons, Ltd.

In multicast communications, where service providers distribute sensitive information such as military operations information, an important issue is to control operation participants' access to transmitted data and network resources. This issue becomes more complicated when multicast communications take place in mobile ad hoc network (MANET) environments. In this study, we discuss the multicast security issues in MANET and propose a new approach for securing the communications under these conditions. Specifically, we determine the security requirements based on certain use case scenarios and define access control policies in mobile ad hoc multicast communications accordingly. The goal is to create a new protocol for multicast data dissemination while enforcing the required data access policies. The protocol that we propose is based on the multicast features of the well-known Ad hoc On-Demand Distance Vector Routing (MAODV) protocol and incorporates the features of the role-based access control model. The study shows that our approach is simple, but yet flexible and effective in controlling the access to data being transmitted within the multicast groups.

The IEEE 802.15.4 standard was developed for the purpose of media access control of low power wireless personal area networks. Wireless sensor network devices have the general characteristics of low power capabilities and operation. From this point of view, it makes technical sense to use the IEEE 802.15.4 standard for the MAC layer of wireless sensor networks. There are several modes of operation of the 802.15.4 protocol. In this study, we explore the use of non-beaconed mode for the operation of these networks. We modify this mode of the protocol in order to make it able to provide reliable delivery of high importance traffic within these networks, and at the same time ensure an energy-efficient operation of the network. We conduct simulation experiments to show the ability of our protocol to deliver highly important data traffic in a robust manner, while saving the energy resources of network nodes.

In this study, we introduce a novel OFDMA-based MAC protocol for underwater acoustic sensor networks. The design approach of this protocol exploits the multi-path characteristics of a fading acoustic channel. The goal is to convert the channel into parallel independent acoustic sub-channels that undergo flat fading. Communication between node pairs within the network is then done using subsets of these sub-channels thus fully utilizing the available limited bandwidth while avoiding interference completely. We derive the mathematical model for optimal sub-channel selection. We also present the results of the experiments that we conducted to evaluate the new protocol and we compare these results to those of a MAC protocol that is based on the CDMA scheme.

In this study, we derive an analytical model that determines the conditions that lead to throughput-starvation in Wimax-based networks. We limit our discussion to downlink traffic in point-to-multipoint (PMP) based networks. The objective of our model is to define the probability of a subscriber station becoming throughput starved under different operating conditions. The model also defines the probability of a subscriber station being throughput overserved under the same operating conditions. We validate the resulting model using simulation experiments.

In this study, we present a performance evaluation of an energy conservation technique designed for mobile ad hoc networks. This technique was designed with energy fairness central to its operation. This algorithm is not a routing algorithm. It works with existing routing protocols to complement their functionality from an energy-efficiency perspective. We show that this technique scales well with increased network traffic and population. We also compare it to the on-demand power management algorithm, a technique specifically designed to reduce idle energy consumption. Our comparison shows that our technique performs better in terms of energy savings and fairness as well as network lifetime extension