Dynamic resource allocation in O-RAN is critical for managing the conflicting QoS requirements of 6G network slices. Conventional reinforcement learning agents often fail in this domain, as their unimodal policy structures cannot model the multi-modal nature of optimal allocation strategies. This paper introduces Diffusion Q-Learning (Diffusion-QL), a novel framework that represents the policy as a conditional diffusion model. Our approach generates resource allocation actions by iteratively reversing a noising process, with each step guided by the gradient of a learned Q-function. This method enables the policy to learn and sample from the complex distribution of near-optimal actions. Simulations demonstrate that the Diffusion-QL approach consistently outperforms state-of-the-art DRL baselines, offering a robust solution for the intricate resource management challenges in next-generation wireless networks.

Accurate and comprehensive data acquisition is critical for modern data-driven environmental applications. Mobile Crowdsensing (MCS) offers an effective approach by leveraging user participation to collect environmental data through task assignment. To minimize costs, MCS platforms often partition the environment into subareas and utilize inference algorithms to extrapolate data for entire subareas based on partial sensing in a limited subset. However, determining the optimal set of users for sensing tasks remains challenging due to constraints such as user availability and the complexity of data inference models. This paper introduces Sensify, a task assignment strategy that optimizes data acquisition by accounting for data correlations and budget constraints. Sensify efficiently selects subareas and recruits cost-effective users for sensing tasks, incorporating user-specific contexts such as location and device power availability during task assignment. To adaptively manage the platform budget, the strategy considers a dynamic set of users with varying costs over time. A deep recurrent reinforcement learning-based network is employed to select optimal subareas for sensing, while user recruitment is dynamically optimized using a reinforcement learning approach. Specifically, a modified Contextual Combinatorial Multi-Armed Bandit (CC-MAB) framework is utilized to handle the volatility and variability in user costs. Experiments conducted on two real-world datasets demonstrate that Sensify can improve data acquisition by up to 7% compared to existing approaches.

The open radio access network (O-RAN) architecture's native virtualization and embedded intelligence facilitate RAN slicing and enable comprehensive end-to-end services in post-5G networks. However, any vulnerabilities could harm security. Therefore, artificial intelligence (AI) and machine learning (ML) security threats can even threaten O-RAN benefits. This paper proposes a novel approach to estimating the optimal number of predefined VNFs for each slice while addressing secure AI/ML methods for dynamic service admission control and power minimization in the O-RAN architecture. We solve this problem on two-time scales using mathematical methods for determining the predefined number of VNFs on a large time scale and the proximal policy optimization (PPO), a Deep Reinforcement Learning algorithm, for solving dynamic service admission control and power minimization for different slices on a small-time scale. To secure the ML system for O-RAN, we implement a moving target defense (MTD) strategy to prevent poisoning attacks by adding uncertainty to the system. Our experimental results show that the proposed PPO-based service admission control approach achieves an admission rate above 80% and that the MTD strategy effectively strengthens the robustness of the PPO method against adversarial attacks.

The next radio access network (RAN) generation, open RAN (O-RAN), aims to enable more flexibility and openness, including efficient service slicing, and to lower the operational costs in 5G and beyond wireless networks. Nevertheless, strictly satisfying quality-of-service requirements while establishing priorities and promoting balance between the significantly heterogeneous services remains a key research problem. In this paper, we use network slicing to study the service-aware baseband resource allocation and virtual network function (VNF) activation in O-RAN systems. The limited fronthaul capacity and end-to-end delay constraints are simultaneously considered. Optimizing baseband resources includes O-RAN radio unit (O-RU), physical resource block (PRB) assignment, and power allocation. The main problem is a mixed-integer non-linear programming problem that is non-trivial to solve. Consequently, we break it down into two different steps and propose an iterative algorithm that finds a near-optimal solution. In the first step, we reformulate and simplify the problem to find the power allocation, PRB assignment, and the number of VNFs. In the second step, the O-RU association is resolved. The proposed method is validated via simulations, which achieve a higher data rate and lower end-to-end delay than existing methods.

The novel concept of Internet of Nano Things (IoNT) brings even larger groups of nanomachines collaborating to achieve more complex tasks in the military, medical, and security fields. Moreover, Internet of Bio-Nano Things (IoBNT) is an emerging technology defining the seamless connection of nanomachines and biological entities with each other where they can access the traditional wireless communication networks to provide novel Internet of Things (IoT) applications, such as health monitoring, healthcare, and targeted therapy. The exchange of information between biological cells is based on the synthesis, transformation, emission, propagation, and reception of molecules. This information exchange is recently classified in telecommunications as molecular communication which is a biologically inspired technique to communicate in very small dimension networks. In nanonetworks, a high number of nanothings will operate in the same medium and they interfere with each other. Multiuser interference will create significant limits. Hence, we introduce to use the direction of releasing molecules as a new property to convey information. Releasing the molecules to the specific directions enhances the performance of molecular communication systems due to multiusers interference mitigation. Hence, adjacent transmitters can convey information in different directions, simultaneously. Then, we propose the binary direction shift keying (BDSK) modulation scheme where the transmitter pumps molecules in two different directions. Next, we obtain the error probability and achievable bit rate of BDSK modulation. Finally, we evaluate the performance of BDSK modulation by numerical results. The result of this article can be useful for molecular communication relay systems where relay nodes convey information to the different destinations.

Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute an extensively-researched machine learning sub-field for the creation of synthetic data through deep generative modeling. GANs have consequently been applied in a number of domains, most notably computer vision, in which they are typically used to generate or transform synthetic images. Given their relative ease of use, it is therefore natural that researchers in the field of networking (which has seen extensive application of deep learning methods) should take an interest in GAN-based approaches. The need for a comprehensive survey of such activity is therefore urgent. In this paper, we demonstrate how this branch of machine learning can benefit multiple aspects of computer and communication networks, including mobile networks, network analysis, internet of things, physical layer, and cybersecurity. In doing so, we shall provide a novel evaluation framework for comparing the performance of different models in non-image applications, applying this to a number of reference network datasets.

In this paper, we combine centralized dynamic-time division duplex (D-TDD) with interference alignment (IA) for a wireless network comprised of$N$full duplex nodes. We maximize the performance of the proposed centralized D-TDD scheme utilizing IA in terms of rate region by optimizing the reception, transmission, simultaneous reception and transmission, and silence at each node in each time slot in addition to the choice of whether a node should treat the interference as noise or it should use IA. The problem of the rate region maximization of the wireless network is formulated as a non-convex optimization problem, whose optimal solution is found. The simulation results demonstrate that the proposed centralized D-TDD scheme utilizing IA achieves significant gains over the existing D-TDD schemes.

Molecular communication is a biologically-inspired technique to communicate in very small dimension networks. Many different modulation schemes were proposed in the literature based on different properties of molecules. In this paper, we propose to use the direction of releasing molecules as a new property to convey information and we propose Direction Shift Keying (DSK) modulation scheme. Then, we obtain a solid analytical formulation for the concentration of molecules in the 3-D diffusion environment when the transmitter employs DSK modulation. We analyze the concentration of molecules by numerical results. It is observed that the concentration of molecules becomes maximum on the receiver surface in the releasing direction and at a specific time. The derived results may be useful for designing molecular communication systems and evaluating the performance of DSK scheme in the future works.

Software-defined networking (SDN) is recognized as a promising solution for the efficient management of numerous devices in the Internet of Things (IoT). To appropriately forward the incoming packets, SDN-enabled devices request the controller for traffic rules resulting in a significant delay. To avoid frequent communication with the controller and reducing the delay, these devices cache the rules as match action pairs in flow tables for a certain amount of time. This is referred to as rule caching. Flow tables are made of ternary content-addressable memories (TCAMs) capable of high-speed parallel lookup. Nonetheless, due to the high expenses and power consumption of TCAMs, flow tables have limited capacity and cannot store rules of all the users. Therefore, the assignment of limited flow tables is a challenge in IoT networks with a large number of users. In this article, we consider an SDN-enabled base station serving a set of users in a cell and is equipped with a finite-capacity flow table. We assume users' traffic obeys a bursty ON-OFF model and formulate the efficient allocation of flow table entries to users over time. It results in a mixed-integer nonlinear program. We introduce a change of variables that turns the problem into an integer linear program. This is still intractable and cannot support problems with large dimensions such as massive IoT networks. To handle its complexity, we design a time-efficient procedure with close-to-optimal performance. Finally, numerical results demonstrate the significant improvement achieved by the proposed scheme.

Network softwarization is one of the major paradigm shifts in the next generation of networks. It enables programmable and flexible management and deployment of the network. Network function virtualization (NFV) is referred to the deployment of software functions running on commodity servers instead of traditional hardware-based middle-boxes. It is an example of network softwarization. In NFV, a service is defined as a chain of software functions named service chain function (SFC). The process of allocating the resources of servers to the services, called service placement, is the most challenging mission in NFV. Dynamic nature of the service arrivals and departures as well as meeting the service level agreement make the service placement problem even more challenging. In this paper, we propose a model for dynamic reliability-aware service placement based on the simultaneous allocation of the main and backup servers. Then, we formulate the dynamic reliability-aware service placement as an infinite horizon Markov decision process (MDP), which aims to minimize the placement cost and maximize the number of admitted services. In the proposed MDP, the number of active services in the network is considered to be the state of the system, and the state of the idle resources is estimated based on it. Also, the number of possible admitted services is considered as the action of the presented MDP. To evaluate each possible action in the proposed MDP, we use a sub-optimal method based on the Viterbi algorithm named Viterbi-based Reliable Static Service Placement (VRSSP) algorithm. We determine the optimal policy based on value iteration method using an algorithm named VRSSP-based Value Iteration (VVI) algorithm. Eventually, through the extensive simulations, the superiority of the proposed model for dynamic reliability-aware service placement compared to the static solutions is inferred.

In this paper, we consider a neuro-spike communication system between two neurons where nano-machines are used to enhance ability of neurons. Nano-machines can be employed for stimulation tasks when neurons have lost their ability to communicate. In the assumed system, information is conveyed via the time intervals between the input spikes train. For efficiency evaluation of temporal coding, we model the neuro-spike communication system by an additive Gamma noise channel. We present this model by considering different time distortion factors in the neuro-spike system. Then, we derive upper and lower bounds on the channel capacity. We analyze the channel capacity bounds as functions of the time intervals between the input spikes and the firing threshold of the target neuron. Moreover, we propose maximum likelihood and maximum a posteriori receivers and derive the resulting bit error probability when the system uses binary modulation. In addition, we obtain an upper bound for this error probability. Then, we extend this upper bound to the symbol error probability of the $T$ -ary modulations. Simulation results show that this upper bound is tight. The derived results show that temporal coding has a higher efficiency than spike rate coding in terms of achievable data rate.

Network function virtualization (NFV) is referred to as the deployment of software functions running on commodity servers, instead of hardware middleboxes. It is an inevitable technology for agile service provisioning in next-generation telecommunication networks. A service is defined as a chain of software functions, named virtual network functions (VNFs), where each VNF can be placed on different host servers. The task of assigning the VNFs to the host servers is called service placement. A significant challenge in service placement is meeting the reliability requirement of a service. In the literature, the problem of service placement and providing the required reliability level are considered separately. First, the main server is selected, and then, the backup servers are deployed to meet the reliability requirement of the service. In this paper, we consolidate these two steps and perform them jointly and simultaneously. We consider a multi-infrastructure network provider (InP) environment where InPs offer general purpose commodity servers with different reliability levels. Then, we propose a programming problem for main and backup server selection jointly minimizing the cost of resources of the InPs and maximizing the reliability of the service. We reformulate this problem as a mixed integer convex programming (MICP) problem. Since MICPs are known to be NP-hard in general, we propose a polynomial time sub-optimal algorithm named Viterbi-based Reliable Service Placement (VRSP). Using numerical evaluations, we investigate the performance of the proposed algorithm compared to the optimal solution resulting from the MICP model and also with three heuristic algorithms.

Network function virtualization (NFV) is referred to the technology in which softwarized network functions virtually run on commodity servers. Such functions are called virtual network functions (VNFs). A specific service is composed of a set of VNFs. This is a paradigm shift for service provisioning in telecom networks which introduces new design and implementation challenges. One of such challenges is to meet the reliability requirement of the requested services considering the reliability of the commodity servers. NFV placement which is the problem of assigning commodity servers to the VNFs becomes crucial under such circumstances. To address such an issue, in this paper, we employ Deep Reinforcement Learning (Deep-RL) to model NFV placement problem considering the reliability requirement of the services. The output of the introduced model determines optimal placement in each state. Numerical evaluations show that the introduced model can significantly improve the performance of the network operator.

The future 5G mobile networks are expected to provide agile services to users with different requirement levels. These services need isolated and independent lifecycle management. Network slicing is considered as a solution for simultaneous onboarding of these services. The main idea behind the network slicing is the split of the resources of the radio access network (RAN) and core network (CN) between different services while the service requirements are met and service operations are independent. Each slice is composed of a set of RAN and CN resources and is assigned to one or some services. One of the important challenges in a slice-based mobile network is the service (or slice) admission control problem. It is referred to the process of determining the policy of the mobile network operator (MNO) for admitting the service requests. However, most of the conducted studies for service admission control problem in network slicing only considers the resources of the RAN layer. In this paper, we model the problem of service admission control considering the CN and RAN layers resource allocation while maximizing the MNO's profit. Due to the boolean programming nature of the proposed problem, we introduce a two-step sub-optimal algorithm. In each step of the proposed algorithm, a heuristic for a variant form of the knapsack problem is solved. Finally, through simulations, the proximity of the proposed algorithm to the optimal solution is inferred.

Free-space optical (FSO) communication offers wireless connectivity with high data rates and low system complexity; however, it suffers from the infrequent adverse weather conditions. This paper proposes a novel hybrid RF/FSO system based on a game theoretic spectrum trading process to enhance the reliability of FSO links. The proposed system is considered to be both spectrum- and power-efficient. It is assumed that no RF spectrum is preallocated to the FSO link and only when the link availability is severely impaired by the infrequent adverse weather conditions, i.e., fog, and so on, the source can borrow a portion of licensed RF spectrum from one of the surrounding RF nodes. A market-equilibrium-based pricing process is proposed for the spectrum trading between the source and RF nodes. By using the leased spectrum, the source is able to establish a dual-hop RF/FSO hybrid link to maintain its throughput to the destination. Our extensive performance analysis illustrates the effectiveness of the proposed communication system. It is demonstrated that the proposed scheme can significantly improve the average capacity of the system, especially when the surrounding RF nodes are with low traffic loads. In addition, the system benefits from involving more RF nodes into the spectrum trading process by means of diversity. Finally, the application of the proposed system in a realistic scenario is presented based on the weather statistics in the city of Edinburgh, U.K., which demonstrates that the system can substantially enhance the link availability toward the carrier-class requirement.

We consider a neuro-spike communication system between two nano-machines, with information conveyed in the time intervals of the input spike train. The main contribution of our paper is modeling of the neuro-spike communication channel by an additive Gamma noise channel model. In this channel, the information is corrupted by Gamma distributed noise. We show that the proposed channel model is efficient for the neuro-spike communication when it exploits temporal modulations to transfer information. Then, we consider the Gamma distributed noise and we derive the upper and lower bounds on the channel capacity. Unlike Additive White Gaussian Noise (AWGN) channels, there is no single quality measure like signal-to-noise ratio for this channel model. Thus, we analyze the channel capacity bounds versus different values of time intervals and the decision threshold of the receiver.

Alternating direction method of multipliers (ADMM) is a popular convex optimisation algorithm, which is implemented in a distributed manner. Applying this algorithm to consensus optimisation problem, where a number of agents cooperatively try to solve an optimisation problem using locally available data, leads to a fully distributed algorithm which relies on local computations and communication between neighbours. In this study, the authors analyse the convergence of the distributed ADMM algorithm for solving a consensus optimisation problem over a lossy network, whose links are subject to failure. They present and analyse two different distributed ADMM‐based algorithms. The algorithms are different in their network connectivity, storage and computational resource requirements. The first one converges over a sequence of networks which are not the same but remains connected over all iterations. The second algorithm is convergent over a sequence of different networks whose union is connected. The former algorithm, compared to the latter, has lower computational complexity and storage requirements. Numerical experiments confirm the proposed theoretical analysis.

Device‐to‐device (D2D) communication is a promising 5G technology, which helps to increase spectrum efficiency and sum‐rate, as well as to decrease experienced latency. The main challenges of D2D communication are power consumption of paired devices, channel estimation between device pairs, and interference management between devices that use the same time and frequency resources. The electromagnetic time reversal (EMTR) technique is used in D2D communications to focus the signal power in both time and space domains for power efficiency of end users, to simplify the structure of transceivers, to help channel estimation in a low complexity way, and to nullify the effect of interference. First, in the time domain, the effect of using EMTR technique is investigated and its performance is compared with a similar non‐EMTR system. Then, EMTR technique is used in an OFDM‐based system and its advantages in the context of signal to interference plus noise ratio (SINR) and sum‐rate are presented analytically and through computer simulations. Simulation results show a significant gain in SINR, sum‐rate and received signal power for the proposed EMTR‐based D2D system.

Millimeter-wave (mm-wave) communication is a promising technology for supporting extremely high data rates in the next generation wireless networks. Mm-wave signals experience high path loss and directional transmission is required to compensate the severe channel attenuation. The special characteristics of the mm-wave propagation arise opportunities as well as challenges for the network resource allocation problems. In this paper, a fair user association, beamwidth selection and power allocation problem for indoor mm-wave networks is studied. The objective of the optimization problem is to maximize the minimum user throughput to provide a fair resource distribution among the users. In our model, we take into account the unique mm-wave communications features, namely beam alignment procedure and directional transmission. Simulation results confirm the superior performance of the proposed solution compared to the existing approaches.

In this paper, we consider the downlink power allocation problem where the objective is to maximize the sum-rate in software-defined radio access network (SDRAN). Using SDRAN, the power allocation is performed at a central controller with the channel state information (CSIs) received from the base stations (BSs). However, due to time variable nature of the fading channel and the control path delay between the controller and BSs, the central decisions at the controller might become outdated by the time each BS wants to transmit. Modeling channel fading by using a time-correlated channel model, the power allocation problem would consist of parameters which are random variables. In order to solve this problem, we use the stochastic optimization framework and propose two practical schemes. The first solution works in a completely centralized manner while the second one is performed collaboratively in the controller and BSs. We analyze these schemes for cellular networks with arbitrary number of BSs and present procedures to find the optimum power allocations. Finally, using different numerical experiments, we evaluate the performance of our proposed schemes. Significant improvement in comparison to the scenario without considering CSI variation is observed.

The throughput performance of a random cooperative medium access control (CoopMAC) network in the presence of shadowing and path loss is considered. The nodes are assumed to be distributed as a homogeneous two‐dimensional Poisson point process with constant intensity. The helpers are divided into several tiers each having a distinct operating region and a distinct cooperative throughput. Then, the conditions under which a helper in a particular tier can improve the transmission rate between a given pair of nodes are examined. Based on these conditions, an exact analytical expression is derived for the average cooperative throughput of a Poisson CoopMAC network that is subject to path loss and shadowing. The expression is then used to investigate the effects of shadowing, intensity of helpers and distance between source and destination nodes on the average cooperative throughput of the network.

The cooperative medium access control (CoopMAC) protocol in the presence of randomly distributed nodes and shadowing is considered. The nodes are assumed to be distributed according to a homogeneous 2-D Poisson point process. A new scheme is proposed for helper selection and throughput performance analysis, which depends on the shadowing parameters as well as the distribution of helpers. In the proposed protocol, the potential helpers are divided into several tiers based on their cooperative transmission rate in a way that the lower the tier index, the higher its priority. When there are several helpers of the same tier, the helper that is less affected by shadowing is chosen for cooperation. The helper tiers are classified into five different classes according to their operating regions. Then, upper and lower bounds are derived for the average cooperative throughput of the proposed CoopMAC scheme by using this classification. It is observed that the proposed scheme readily outperforms the conventional CoopMAC protocol in having larger average throughput. It is also seen that the cooperative throughput of the proposed scheme approaches the upper bound when the density of nodes increases.

Deployment of femtocells have emerged as a promising technique to address the need for the exponentially increasing mobile traffic demand. They can also improve the capacity and coverage for the indoor wireless users. However, the cross-tier interference in such heterogeneous networks between the femtocells and the macrocells is a design challenge which should be answered before such deployment. In this paper, a spectrum-sharing OFDMA femtocell network is considered in which the macrocell base station protects itself and its users by pricing the interference from femtocell access points. A price-based resource allocation is formulated as a Stackelberg game model to jointly maximize the revenue of the macrocell and the utility of the femtocell users (FUEs) while a predetermined tolerable cross-interference constraint at the macrocell is met. In the proposed model, the macrocell determines the subchannel assignment and the interference prices for the FUEs. Then, the FUEs choose their optimal downlink power. To find the Stackelberg equilibrium, the optimization problems of the leader and the followers are solved. For the followers' sub-game, a closed form expression is obtained. To find the leader's sub-game solution, the non-convex mixed integer nonlinear (MINLP) problem is first converted to an equivalent convex MINLP. Then, we employ the outer approximation (OA) to iteratively and efficiently solve the leader problem. The numerical results validate the convergence of the OA algorithm and the operation of our price-based scheme.

Millimeter-wave (mm-wave) communication is a promising technology for the next generation cellular networks. Motivated by the huge available bandwidth at these bands, it can be used to support the fastest-growing demands for the mobile data traffic such as multimedia applications. However, there are some challenges on the network connectivity at the mm-wave frequency bands. The high path loss, the limited diffraction capability due to the short wavelength, and having difficulties in penetrating through solid materials necessitate the line-of-sight and multi-hop communication in mm-wave networks. In this paper, we develop a multi-hop routing protocol which maximizes the sum quality of the uncompressed high-definition video applications for the device-to-device connections. The quality is measured as a function of the rate in this paper. We take into account the unique characteristics of the mm-wave propagation in our model. Simulation results show that the proposed algorithm achieves the optimal solution for high cooperation probability. It is also verified by the simulation that our algorithm outperforms the max-min flow routing protocol solution.

In this paper, we propose a novel online centralized algorithm for enabling non-cooperative and energy harvesting capable base stations (BSs) to trade energy in multi-tier cellular networks. BSs are connected to the non-renewable energy source used by a BS when it cannot harvest enough energy to serve its connected users. A double auction trading framework is proposed to motivate BSs with the extra harvested energy to share their surplus energy with BSs that have not harvested sufficient energy. In addition, BSs with energy deficit are stimulated to buy surplus energy of other BSs which results in reducing of the non-renewable energy consumption. The algorithm satisfies truthfulness, individual rationalities and budget balance. Moreover, it reaches the Nash equilibrium. The extra harvested energy is distributed by the smart grid that prevents energy accumulation which results in the waste of the harvested energy due to limited battery capacities. To reduce smart grid usage in distributing energy, an optimization is embodied in the proposed algorithm to assign BSs with energy deficit to near BSs with extra harvested energy. Simulations results show that the non-renewable energy consumption reduces dramatically when the algorithm is applied. In addition, BSs gain more profit, consequently, their utility functions enhance.

The demand for multimedia services in modern networks has experienced exponential growth in recent years and it is expected to dominate the mobile traffic in near future. On the other hand, the link capacity of mobile networks is limited due to the scarce wireless resources. Caching is a popular technique that uses available storage capability of the mobile devices to relieve this traffic tension in high peak hours of network operation. In this paper, we investigate the effect of mobility on a wireless device-to-device (D2D) coded caching architecture. Coded caching is a technique in which library files are split into sub-files and any combination of sub-files can be cached at devices and exchanged between them later. We consider two mobility models for our network architecture and study the effect of mobility on the throughput of the network. We show that, in contrast to the static scenario, by exploiting the mobility in a D2D coded caching network, the coded multi-casting gain and the spatial reuse gain can be attained simultaneously, in terms of the throughput scaling law.

Millimeter-wave (mm-wave) communication is a promising technology for the next generation wireless networks. Motivated by the immense amount of bandwidth at these bands, it can be used to support the quality of service requirements for the bandwidth-intensive purposes, like the backhaul demands of the small cell base stations. However, the high path loss, the limited penetration ability and the intermittent connectivity necessitate utilizing the multi-hop transmission techniques to maintain network connectivity. In this paper, the outage performance of the mm-wave relaying systems is studied. We take into account the unique propagation characteristics of the mm-wave bands, namely the intermittent connectivity, and obtain the closed-form expression for the outage probability of the mm-wave multi-hop regenerative relaying system. Moreover, the closed-form approximation for the outage probability in a dual-hop nonregenerative case is also derived. The analytical expressions are verified by the simulation results.

With multimedia dominating the main traffic load of the wireless networks, device-to-device (D2D) communication is an efficient way of data offloading. By the growth of video data traffic, quality of experience (QoE), which is a user-centric measure, has become the focus of the research academia. In this paper, the video quality and fluency are considered as the factors that influence the user experience in D2D video streaming services. Aiming to enhance the user experience, we propose a QoE-aware power allocation for D2D video transmissions. The resource allocation target is to maximize the video quality while minimizing the data rates variations over the time-varying wireless channels. We then show that using dual decomposition technique, the problem can be implemented in a distributed way by the exchange of demand and price among the network and D2D users. Simulation results demonstrate that the proposed scheme can significantly improve the QoE in D2D video services.

Deployment of small cells is considered as one of the most promising solutions for increasing the capacity and coverage of the wireless networks. The networks consisting of different base station levels are called heterogeneous cellular networks (HCN). In such networks, cell range expansion is applied for offloading more users from the macro base station (MBS) to the femtocell access point (FAP). In this paper, we use the structure of HCNs for transmission of scalable video coding (SVC). For two-layer spatial scalable video, due to the umbrella coverage of a macrocell, MBS is suitable for transmission of the base layer content to the users within its coverage. Moreover, the enhancement layer is transmitted via the FAP because of its high data rate. Using the tools from stochastic geometry, we derive the rate distributions of the users receiving the base layer. Moreover, we quantify two performance metrics, which are the standard-definition (SD) outage probability and the high-definition (HD) probability. We prove that offloading more users by the cell range expansion and resource partitioning improve the HD probability.

In order to compensate inefficiency of traditional macrocell base stations (MBS), femtocell base stations (FBS) are deployed in cell areas. This deployment can enhance the Quality of Service (QoS) for the users which have difficulty communicating with the MBS. However, the presence of FBSs causes interference for MBS that should be controlled. Guaranteed QoS such as delay-bounds is required in real-time video applications. Moreover, the rapid growth of mobile-video traffic and time varying nature of wireless channels make it difficult to guarantee stringent delay constraints. However, providing delay-bounds based on effective capacity looks more appropriate for unreliable channels. This paper proposes a power allocation scheme based on Nash Bargaining Solution(NBS). NBS is a cooperative solution for Nash bargaining competitive game, which leads to a fair resource allocation and also satisfies the Pareto efficiency. We derive NBS in a tractable closed form formula by taking into account the delay-bounds and interference constraints. Analyzing the simulation results demonstrates that our proposed solution reaches a high level of fairness among users.

Heterogeneous cellular networks are a promising approach in achieving better quality of service and coverage. They are also a relief for the exponentially increasing data traffic demand. Small cell access points (SAPs) typically rely on out-ofband communication links for backhaul which can be wired or wireless. In this paper, we investigate the price-based resource allocation problem in a self-backhauled SAP. In our scenario, an imperfect full duplex SAP uses the same radio access spectrum for both access and backhaul links to forward the traffic of users from a base station to the user equipments (UEs). In our system model, the SAP charges each UE proportional to the amount of the power it uses to transmit to that UE. A Stackelberg game model is proposed to model and investigate the joint utility maximization problem of the SAP and UEs. We will assume an aggregate transmit power constraint in the SAP. In our game model, the SAP is leader and UEs are the followers. We formulate the utility maximization problems of both leader and followers as optimization problems. We prove that both sub-games are convex problems which ensures their tractability.We also propose a novel algorithm to obtain the Stackelberg equilibrium of the game. Numerical results validate our proposed priced based resource allocation scheme.

In machine to machine (M2M) communication systems based on the Third Generation Partnership Project (3GPP) Long Term Evolution (LTE), the machine type communication (MTC) devices compete in a random access channel (RACH) to access the network. An MTC device randomly chooses a preamble from a pool of preambles and transmits it during the RACH. The evolved node B (eNodeB) acknowledges the successful reception of a preamble if that preamble is transmitted by only one device. To reduce the burstiness of the connection requests in heavy traffic situations, access class barring (ACB) is proposed in the 3GPP standard. Using ACB, an MTC device postpones its request in a RACH with a probability p. In this paper, we propose a new adaptive ACB scheme for congestion control of bursty M2M traffic. The optimal value of the ACB depends on the total number of MTC devices competing in a RACH. To estimate this number, we derive a joint conditional probability distribution function (PDF) for the number of preambles selected by zero or one MTC device, conditioned on the number of MTC devices that passed the ACB check. We design a maximum likelihood estimator using this PDF. We use this estimation to dynamically adjust the ACB factor. To further improve our estimation, we use Kalman filtering based on the dynamics of the system. Numerical results show that the total service time for the proposed method is very close to the optimal case where the information of the number of MTC devices is given.

In this paper, a novel active queue management scheme is proposed to control the congestion in the networks using TCP/IP protocol for transport layer. Active queue management (AQM) is a complement protocol to TCP/IP which provides feedback to the TCP sources in order to perform close loop congestion control. We use the nonlinear model of TCP/AQM system and build our controller on this realistic model. The control parameter is the drop probability of AQM technique. Our controller is designed based on integral backstepping technique. The main advantage of our AQM technique compared to other is the robustness of the algorithm with respect to changes in the system parameters such as the number of sources of the round trip time. Next, we provide conditions which guarantee that the control input which is the packet dropping probability falls between 0 and 1. Finally, we evaluate the performance of our proposed scheme for various network conditions and parameters. Compared to the other AQM controllers, the simulation results obtained with Simulink, demonstrate the effectiveness of the proposed method.

Wireless field networks (WFNs) are type of ad hoc network which are employed for industrial automation applications. Delay plays an important role due to the time sensitive nature of such applications. In this paper, we build scheduling algorithms based on WFN standards such as WirelessHART and ISA 100.11a. The goal is to minimize the delay. Two heuristic scheduling algorithms are presented. The first algorithm is proposed based on finding maximum independent set (MIS). Advantage of this algorithm is its low complexity and ease of implementation, while not essentially optimizing the delay. The second algorithm minimizes the total delay of WFNs. Also, it controls the delay of each field node. Another objective which is surveyed in formulation of problem is to minimize the number of channels used in scheduling.

Deploying small cells in cellular networks, as a technique for capacity and coverage enhancement, is an indispensable characteristic of future cellular networks. In this paper, a novel online decentralized algorithm for enabling energy trading in multi-tier cellular networks with selfish energy harvesting capable base stations (BSs) is proposed. A BS uses the non-renewable energy when it cannot harvest sufficient energy to serve its connected users. To minimize the non-renewable energy consumption, we establish a framework for trading energy such that BSs with energy deficit are stimulated to compensate their energy shortage with the extra harvested energy of other BSs. BSs with energy deficit are assigned to BSs with extra harvested energy by using matching theory. The extra harvested energy is distributed by the smart grid. Along with energy trades, BSs gain more profit and their utility functions enhance. Simulation results show that the waste of energy due to limited batteries and the non-renewable energy consumption decreases considerably when the proposed algorithm is applied.

In this paper, we propose a novel online centralized algorithm for energy cooperation among energy harvesting capable base stations (BSs) in multi-tier cellular networks. BSs are connected to the non-renewable source used by a BS when it cannot harvest sufficient energy to serve its connected users. BSs with the extra harvested energy operate cooperatively and share their surplus energy with BSs that have not harvested sufficient energy. To stimulate BSs with energy deficit to use the shared energy of other BSs, an energy pricing framework is established which results in reducing of the non-renewable energy consumption. We formulate the problem of maximizing the fairness of the renewable energy distribution. The closed-form of energy share given to each BS with energy deficit is found, by which the renewable energy distribution fairness is maximized. Energy is shared by the smart grid. The problem of minimizing the smart grid usage cost for distributing energy is formulated and an online algorithm is proposed to approximate its solution. Simulation results show that the approximate algorithm reduces the non-renewable energy consumption significantly and reduces the cost of smart grid usage near to the optimal solution.

Transport layer protocol (TCP) is used to avoid the congestion in the network by controlling the rate at which the source inject traffic to the network. In order to control their internal buffers and avoid overflow, the intermediate routers of a network send control signals (feedbacks). The source adjusts its transmission rate accordingly. Different control signals have proposed in the literature but the most widely used is performed by dropping the packet. When a packet is dropped, the source does not receive it acknowledgement and is notified of congestion at the network. Active queue management (AQM) is referred to the technique where intermediate routers by employing that can control and stabilize their internal routers and avoid network congestion. In many cases, an AQM technique defines the decision algorithm for dropping the packets of a source. In this paper, we consider the AQM in the network with one wireless access link. Active queue management is more complicated in the wireless networks than that in wired ones due to the changes in the link capacity and packet erasure in the communication channel. The main contribution of our study is to design a robust queue management based on two degree of freedom internal model control for a network consisting of a wireless link with variable capacity, packet loss, and large delay. Simulation results validate the analytical results through which it is proved that the procedure not only can restrict the fluctuations caused by the fading and packet error rate but also can control the queue length in the case of variation in the number of the users.

The theory of compressive sensing has shown that with a small number of samples from random projections of a sparse signal, one can recover the original signal under certain conditions. In this paper, we use compressive sensing to design a random access protocol for requesting uplink data channels. A wireless node transmits a pseudo-random sequence to an access point (AP) when it requires an uplink channel. The AP receives multiple sequences in a random access shared channel. Due to different propagation delays, the received signals from different wireless nodes are not synchronized at the receiver. Assume that the number of sequence transmissions is substantially less than the number of wireless nodes in the system. Under such circumstances, we design an asynchronous compressive sensing based decoder to recover the original signals in a random access setting. The key difference between our proposed decoder and those presented in the literature is that we do not require any synchronization before sequence transmission which makes our approach practical. Simulation results show the throughput improvement of our proposed scheme compared to two other random access protocols.

In this paper, we propose TCP Vegas with online network coding (TCP VON), which incorporates online network coding into TCP. It is shown that the use of online network coding in transport layer can improve the throughput and reliability of the end-to-end communication. Compared to generation based network coding, in online network coding, packets can be decoded consecutively instead of generation by generation. Thus, online network coding incurs a low decoding delay. In TCP VON, the sender transmits redundant coded packets when it detects packet losses from acknowledgement. Otherwise, it transmits innovative coded packets. We establish a Markov chain to analytically model the average decoding delay of TCP VON. We also conduct ns-2 simulations to validate the proposed analytical model. Finally, we compare the delay and throughput performance of TCP VON and automatic repeat request (ARQ) network coding based TCP (TCP ARQNC). Simulation results show that TCP VON outperforms TCP ARQNC in terms of the average decoding delay and network throughput.

In this paper, we study the use of intra-session random linear network coding (RLNC) in wireless sensor networks. In RLNC, intermediate nodes buffer the packets received from upstream nodes. Using intra-session RLNC, intermediate nodes transmit coded packets by performing coding on the packets of various flows. The main challenge in using intra-session RLNC is to determine how many coded packets each node requires to transmit for each flow such that the sink can decode the packets of all the flows. We analytically find the time at which a node can stop transmission of packets for a particular flow without interrupting the decoding process at the sink. Using this analysis, we design a link-by-link feedback mechanism to acknowledge the packets of a particular flow. An intermediate node generates feedback packets for its upstream nodes. The upstream node stops transmission of a flow when it receives an acknowledgment for that flow. Simulation results show that the link-by-link feedback mechanism with intra-session RLNC achieves lower power consumption compared to RLNC without intra-session coding and also the CCACK algorithm [1].

Random linear network coding (RLNC) can improve the reliability of end-to-end communication in the presence of erasure channels. In RLNC with intra-session coding, an intermediate node creates coded packets by combining the packets of various sources destined for a destination. Each transmitted packet contains information of multiple sources. In this paper, we study the decoding delay of RLNC with intra-session coding for a line network with S sources and a destination. Given the generation size K, we show that the expected decoding delay is upper-bounded by √(η2K log(S)) + Kη1+ η, where η, η1, η2are constants. The analytical results are validated via simulations. We also compare the results with the case where intra-session coding is not used.

Radio-frequency identification (RFID) is a ubiquitous wireless technology which allows objects to be identified automatically. An RFID tag is a small electronic device with an antenna and has a unique serial number. Using RFID tags can simplify many applications and provide many benefits. Meanwhile, the privacy of the customers should be taken into account. A potential threat for the privacy of a user is that of anonymous readers obtaining information about the tags in the system. The use of a blocker tag has been proposed as a solution to avoid unwanted tag interrogations. A blocker tag can simulate all or a portion of tag IDs in the system. This prevents the malicious readers from identifying the tags and obtaining information from the system. Although this solution is simple to implement and has a low cost, it may add another threat to the RFID system if used as a malicious tool to attack the system. A malicious blocker tag can deteriorate the performance of an RFID system by simulating fake tag IDs. In this paper, we study the use of blocker tags for malicious attacks that can prevent nearby legitimate readers from correctly receiving the reply messages from the tags. The blocker attack is a medium access control (MAC)-layer denial of service (DoS) threat and we propose a lower-layer solution for this attack. We mathematically model the blocker attack for RFID systems which operate based on the binary tree walking or ALOHA singulation techniques. Using the developed analytical framework, we propose a probabilistic blocker tag detection (P-BTD) algorithm to detect the presence of an attacker in the RFID system. The P-BTD algorithm can detect the existence of a blocker tag using the information extracted from the interrogations performed by the reader. Simulation results show that our proposed algorithm has a better performance than the threshold-based detection algorithm in terms of the number of required interrogations.

Radio frequency identification (RFID) is an emerging technology for automatic object identification. An RFID system consists of a set of readers and several objects, with each object equipped with a small chip, called a tag. In this paper, we consider the anonymous cardinality estimation problem in an RFID system consisting of several readers. To achieve complete system coverage and increase the accuracy of measurement, multiple readers with overlapping interrogation zones are deployed. We study the problem under two different circumstances. First, we assume that the readers cannot perform interrogations synchronously. This models the case when the readers are not equipped with accurate clocks or synchronization imposes a high overhead. Under such condition, we propose an asynchronous exclusive estimator to estimate the number of tags that are exclusively located in the zone of a selected reader. By using this estimator, we propose an asynchronous multiple-reader cardinality estimation (A-MRCE) algorithm. In the second scenario, we assume that readers can perform interrogations synchronously. We propose a synchronous exclusive estimator and a synchronous multiple-reader cardinality estimation (S-MRCE) algorithm to estimate the total number of tags. For the exclusive estimators, we show that they are asymptotically unbiased and we derive upper bounds on the variance of error. We validate our analytical model via simulations. Results show that although the A-MRCE algorithm enjoys the asynchronous operation of the readers, it performs worse than the S-MRCE algorithm in terms of estimation error. Compared to the enhanced zero-based (EZB) and lottery frame (LoF) algorithms, the variance of the estimation error for both A-MRCE and S-MRCE algorithms increases linearly with the number of readers, while it increases exponentially for EZB and LoF algorithms.

Due to fading and interference, data transmission via wireless links may sometimes be prone to error. For some applications in wireless sensor networks, it is of interest to monitor the link status and infer the packet loss rate. It has been shown that randomized network coding can improve the reliability of wireless sensor networks with lossy links. With network coding, the loss rate of a chosen path in a wireless sensor network is the maximum link loss rate among all the links in that path. This behavior changes the link identification problem and imposes challenges on the link loss inference. In this paper, we study the passive loss tomography problem in coded packet wireless sensor networks. We show that by inspecting the content of the coded packets at the sink (i.e., destination), one can estimate the path loss rates not only from the source nodes but also from various intermediate nodes to the sink. By utilizing such information at the sink, we determine the set of links whose loss rates can be identified. We propose a passive loss inference with random linear network coding (PLI-RLC) algorithm to estimate the link loss rates. Results show that in coded packet wireless sensor networks, our proposed algorithm can identify the status of a higher number of links compared to a Bayesian inference algorithm.

Loss tomography aims to infer link loss rates using end-to-end measurements. We investigate active loss tomography on mesh topologies. When network coding is applied, based on the content of the received probe packet, a receiver should distinguish which paths have successfully transmitted a probe and which paths have not. We establish a lower bound on probe size which is necessary for obtaining such end-to-end observations. Furthermore, we propose a linear algebraic (LA) approach to developing consistent estimators of link loss rates. Our approach exploits the inherent correlation between the losses on links and the losses on different sets of paths, so that the estimators converge to the actual loss rates as the number of probes increases. We also prove that the identiflability of a link is a necessary and sufficient condition for the consistent estimation of its loss rate. Simulation results show that the LA approach achieves better estimation accuracy than the belief propagation (BP) algorithm, after sending reasonably sufficient probes.

Radio frequency identification (RFID) is a ubiquitous wireless technology which allows objects to be identified automatically. An RFID tag is a small electronic device with an antenna and has a unique serial number. In this paper, we study the use of blocker tags by malicious attackers which can cause the nearby readers not being able to successfully receive the reply messages from the RFID tags. We mathematically model the blocker tag attack problem using information extracted from the interrogations performed by the reader. Using this analytical framework, we propose a probabilistic blocker tag detection (PBTD) algorithm to detect the presence of an attacker in the system. The probability of false alarm for the P-BTD algorithm is determined via simulation. Simulation results show that our proposed algorithm has a better performance than the threshold-based detection algorithm in terms of using a shorter time (i.e., fewer interrogations) to detect the presence of blocker tags.

Radio frequency identification (RFID) is an emerging wireless communication technology which allows objects to be identified automatically. An RFID system consists of a set of readers and several objects, equipped with small and inexpensive computer chips, called tags. In a dense RFID system, where several readers are placed together to improve the read rate and correctness, readers and tags can frequently experience packet collision. High probability of collision impairs the benefit of multiple reader deployment and results in misreading. A common approach to avoid collision is to use a distinct frequency channel for interrogation for each reader. Various multi-channel anti-collision protocols have been proposed for RFID readers. However, due to their heuristic nature, most algorithms may not achieve optimal system performance. In this paper, we systematically design two optimization-based distributed channel selection and randomized interrogation algorithms for dense RFID systems. For this purpose, we develop elaborate models for the reader-to-tag and reader-to-reader collision problems. The first algorithm is fully distributed and is guaranteed to find a local optimum of a max-min fair resource allocation problem for RFID systems. The second algorithm is semi-distributed and achieves the global optimal system performance. Max-min fair optimality balances the performance and the processing load among readers. Simulation results show that our algorithms have significantly better performance than the previous heuristic algorithms.

In this paper, we study the problem of supporting multicast traffic in wireless sensor networks with network coding. On one hand, coding operations can reduce power consumption and consequently improve the network lifetime. On the other hand, performing network coding requires the use of the limited resources of the sensor nodes such as memory and energy. We study the tradeoff between maximizing the network lifetime and minimizing the number of network coding operations. We introduce the coding flow variables which enable us to determine the rate at which different operations (e.g., forwarding, replication, and coding) are performed in each sensor node. Using the coding flow variables, we formulate the maximum-lifetime minimum-resource (MLMR) coding subgraph problem as a linear programming problem. The objective in MLMR problem is to jointly maximize the network lifetime and minimize the rate of performing network coding. We propose an MLMR algorithm in order to obtain the optimal coding subgraph. We investigate the lifetime-resource tradeoff assuming that the cost of performing network coding varies for intermediate nodes. Simulation results show that the network lifetime can considerably be improved when the cost of performing network coding is relatively low compared to the case that this cost is high for intermediate nodes in the network. Moreover, results show that the network lifetime can substantially be increased using MLMR algorithm compared with the classical multicast with Steiner tree and another algorithm which uses network coding without considering the broadcast nature of wireless links.

A radio frequency identification (RFID) system consists of a set of readers and several objects, equipped with small computer chips, called tags. In a dense RFID system, where several readers are placed together to improve the read rate and correctness, readers and tags can frequently experience packet collision. A common approach to avoid collision is to use a distinct frequency channel for interrogation for each reader. Various multi-channel anti-collision protocols have been proposed for RFID readers. However, due to their heuristic nature, most algorithms may not fully utilize the achievable system performance. In this paper, we develop an optimization-based distributed randomized multi-channel interrogation algorithm, called FDFA, for large-scale RFID systems. For this purpose, we develop elaborate models for reader-to-tag and reader-to-reader collision problems. FDFA algorithm is guaranteed to find a local optimum of a max-min fair resource allocation problem to balance the processing load among readers. Simulation results show that FDFA has a significantly better performance than the existing heuristic algorithms in terms of the number of successful interrogations. It also better utilizes the frequency spectrum.

In this paper, we study the anonymous cardinality estimation problem in radio frequency identification (RFID) systems. To preserve privacy and anonymity, each tag only transmits a portion of its ID to the reader when it is being queried. To achieve complete system coverage and increase the accuracy of measurement, multiple readers with overlapping interrogation zones are deployed. The cardinality estimation problem is to estimate the total number of tags (or the tag population) in an RFID system. We first propose an exclusive estimator to estimate the number of tags that are exclusively located in the interrogation zone of a selected reader. We then present a multiple-reader tag estimation (MRTE) algorithm that can accurately estimate the tag population using the measurement from different readers and the exclusive estimator. The accuracy of our proposed algorithm and the approximation are validated via simulations. We compare our proposed MRTE algorithm with the enhanced zero-based (EZB) and maximum a posteriori tag estimation (MPTE) algorithms. Although the mean of the estimation error for all three algorithms approaches zero under certain circumstances, the variance of the estimation error for MRTE algorithm increases linearly with the number of readers while it increases exponentially for EZB and MPTE algorithms.

It has been shown that network coding can lead to significant improvement in network capacity and reduction in power consumption for multicast traffic in wireless networks. In this paper, we study the problem of supporting multicast in wireless sensor networks. The objective is to jointly maximize the network lifetime and minimize the number of packets undergoing network coding. We formulate the problem of establishing coding subgraph in the network as a linear programming problem, which is suitable for distributed implementation. We propose a new set of information flow variables, which enables us to determine the rate of performing network coding. Simulation results show that the network lifetime achieved by our proposed scheme is 20% higher than the maximum lifetime Steiner tree algorithm.

Wireless sensor networks (WSNs) have recently received increasing attention from research and development communities. In a WSN, the field information (e.g., temperature, humidity, airflow) is acquired via several battery-equipped wireless devices and is relayed towards a sink node. As the size of the WSNs increases, it becomes inefficient to gather all information in one sink. To tackle this problem, the number of sinks can be increased. The data information flow towards each of the sinks is called a commodity. In this paper, we formulate a lexicographically optimal commodity lifetime (LOCL) routing problem. A stepwise algorithm is proposed to obtain the optimal routing solution which can lead to lexicographical fairness among commodity lifetimes. Simulation results show that our proposed algorithm increases the normalized commodity lifetime compared to MLMS [1] and LMM [2] routing algorithms.

The maximum lifetime routing problem in wireless sensor networks has received increasing attention in recent years. One way is to formulate it as a linear programming problem by maximizing the time at which the first node runs out of energy subject to the flow conservation constraints. The solutions in this problem correspond to the rates allocated to each link. In this paper, we first show that, under certain conditions, the solutions of this problem are not unique for some network topologies. Given the feasible solutions set, one can further define a secondary optimization problem by minimizing the end-to-end packet transfer delay or power consumption. Rather than solving two sequential optimization problems, in this paper, we propose the use of a regularization method which can jointly maximize the network lifetime and minimize another objective (e.g., packet delay). We describe the fully distributed implementation and provide performance comparisons with other algorithms.

In this paper a combination of routing and power management within the context of wireless ad-hoc sensor networks is considered. Using lower layer information (e.g. collision that is considered in MAC layer) is another novel concern which is used in routing decisions. More specifically in this paper we introduce a cross-layer design in which each node dynamically chooses the number of its neighbors and adjusts its power just enough to reach its farthest determined neighbor. In this algorithm the power of transmission and collision are used as metrics for routing and the number of neighbors in which the total energy consumption in data transmission will be the minimum is selected.

In this paper a modified neighbor aware medium-access control protocol is proposed designed for wireless sensor networks. Wireless sensor networks are generally comprised of battery powered nodes. These nodes are densely deployed in an ad-hoc structure. The main objective for sensor networks is low power consumption while latency is less important. This leads different MAC layer design for wireless sensor networks compared to conventional ad hoc protocols. In this paper we propose a neighbor aware medium access control to reduce power consumption. This protocol is derived from modified distributed mediation device (MDMD) protocol and uses useful properties of this protocol while improves the performance of this protocol especially in dense environments. We add a neighbor aware dynamic scheduling scheme to MDMD protocol to reduce power consumption. We compare this modified protocol with sensor-MAC (S-MAC) and MDMD protocol.

In many sensor network (SN) applications it is necessary to provide reliable, fast and full sensing coverage to a sensitive area while at the same time minimizing energy consumption. This paper proposes a new flexible medium-access control (MAC) protocol designed to address this problem. The primary objective of the SN is to achieve low-power consumption while latency is usually less important compare to traditional wireless networks. This characteristic of SN motivates the design of a new MAC layer so that power consumption is reduced. In this paper, a novel traffic-aware algorithm based on a distributed node schedule management (DMD) protocol is introduced that dramatically increases energy saving, especially in intermediate devices inside a multi-hop network. In addition, high QoS is providing for important areas. Support of QoS in MAC layer reduced the routing overhead significantly. The simulation results show noticeable power consumption improvements compared to 802.11-like, sensor MAC and dynamic SMAC protocols, especially in the large scale, which has been achieved by a tradeoff of more latency.