International audienceAd hoc networks are wireless mobile networks that can operate without infrastructure and without centralized networkmanagement. Traditional techniques of routing are not well adapted. Indeed, their lack of reactivity with respect to thevariability of network changes makes them difficult to use. Moreover, conserving energy is a critical concern in the design ofrouting protocols for ad hoc networks, because most mobile nodes operate with limited battery capacity, and the energydepletion of a node affects not only the node itself but also the overall network lifetime. In all proposed single-path routingschemes a new path-discovery process is required once a path failure is detected, and this process causes delay and wastage ofnode resources. A multipath routing scheme is an alternative to maximize the network lifetime. In this paper, we propose anenergy-efficient multipath routing protocol, called AOMR-LM (Ad hoc On-demand Multipath Routing with LifetimeMaximization), which preserves the residual energy of nodes and balances the consumed energy to increase the networklifetime. To achieve this goal, we used the residual energy of nodes for calculating the node energy level. The multipathselection mechanism uses this energy level to classify the paths. Two parameters are analyzed: the energy threshold beta and thecoefficient alpha. These parameters are required to classify the nodes and to ensure the preservation of node energy. Our protocolimproves the performance of mobile ad hoc networks by prolonging the lifetime of the network. This novel protocol has beencompared with other protocols: AOMDV and ZD-AOMDV. The protocol performance has been evaluated in terms of networklifetime, energy consumption, and end-to-end delay
When several radio access technologies (e.g., HSPA, LTE, WiFi and WiMAX) cover the same region, deciding to which one mobiles connect is known as the Radio Access Technology (RAT) selection problem. To reduce network signaling and processing load, decisions are generally delegated to mobile users. Mobile users aim to selfishly maximize their utility. However, as they do not cooperate, their decisions may lead to performance inefficiency. In this paper, to overcome this limitation, we propose a network-assisted approach. The network provides information for the mobiles to make more accurate decisions. By appropriately tuning network information, user decisions are globally expected to meet operator objectives, avoiding undesirable network states. Deriving network information is formulated as a Semi-Markov Decision Process (SMDP), and optimal policies are computed using the Policy Iteration algorithm. Also, and since network parameters may not be easily obtained, a reinforcement learning approach is introduced to derive what to signal to mobiles. The performances of optimal, learning-based, and heuristic policies, such as blocking probability and average throughput, are analyzed. When tuning thresholds are pertinently set, our heuristic achieves performance very close to the optimal solution. Moreover, although it provides lower performance, our learning-based algorithm has the crucial advantage of requiring no prior parameterization. Index Terms-Radio access technology selection, semi-Markov decision process, reinforcement learning, heterogeneous cellular networks. I. INTRODUCTION T HE demand for high-quality and high-capacity radio networks is continuously increasing. It has been reported that global mobile data traffic grew by 81 percent in 2013 [1]. Furthermore, monthly mobile traffic is forecast to surpass 15 exabytes by 2018, nearly 10 times more than in 2013 [1]. Along with this impressive growth, mobile operators are urged to intelligently invest in network infrastructure. They also need to reconsider their flat-rate pricing models [2], seeking positive return-on-investment.
International audienceIn heterogeneous wireless networks, different radio access technologies are integrated and may be jointly managed. To optimize network performance and capacity, efficient Common Radio Resource Management (CRRM) mechanisms need to be defined. This paper tackles the Radio Access Technology (RAT) selection, a key CRRM functionality, and proposes a hybrid decision framework that dynamically integrates operator objectives and user preferences. Mobile users are assisted in their decisions by the network that broadcasts cost and QoS information. Our hybrid approach involves two interdependent decision-making processes. The first one, on the network side, consists in deriving appropriate network information so as to guide user decisions in a way to meet operator objectives. The second one, where individual users combine their needs and preferences with the signaled network information, consists in selecting the RAT to be associated with in a way to maximize user utility. We first focus on the user side and present a satisfaction-based multi-criteria decision-making method. By avoiding inadequate decisions, our algorithm outperforms existing solutions and maximizes user utility. Further, we introduce two heuristic methods, namely the staircase and the slope tuning policies, to dynamically derive network information in a way to enhance resource utilization. The performance of each decision-making process, on network and user sides, is evaluated separately through extensive simulations. A comparison of our hybrid approach with six different RAT selection schemes is also presented
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