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Abstract-In this paper, a novel framework for power-efficient, cluster-based machine-to-machine (M2M) communications is proposed. In the studied model, a number of unmanned aerial vehicles (UAVs) are used as aerial base stations to collect data from the cluster heads (CHs) of a set of M2M clusters. To minimize the CHs' transmit power while satisfying the rate requirements of M2M devices, an optimal scheduling and resource allocation mechanism for CH-UAV communications is proposed. First, using the queue rate stability concept, the minimum number of UAVs as well as the dwelling time that each UAV must spend for servicing the CHs are computed. Next, the optimal resource allocation for the CH-UAV communication links is determined such that M2M devices rate requirements are satisfied with a minimum transmit power. Simulation results show that, as the packet transmission probability of machines increases, the minimum number of UAVs required to guarantee the queue rate stability of CHs will also significantly increase. Our results also show that, compared to a case with pre-deployed terrestrial base stations, the average transmit power of CHs will decrease by 68% when UAVs are used.

Currently, the increasing rate of routing lookups in Internet routers, the large number of prefixes and also the transition from IPV4 to IPV6, have caused Internet designers to propose new lookup algorithms and try to reduce the memory cost and the prefix search and update procedures times. Recently, some new algorithms are proposed trying to store the prefixes in a balanced tree to reduce the worst case prefix search and update times. These algorithms improve the search and update times compared to previous range based trees. In this paper it is shown that there is no need to treat the prefixes as ranges. It is only required to compare them like scalar values using a predefined rule. The method "Scalar Prefix Search" which is presented here, is built on this concept and combining it with the proposed store and search methods, interprets each prefix as a number without any encoding, the need to convert it to the prefix end points or to use the Trie based algorithms whose performance completely depends on IP address length. This method can be applied to many different tree structures. It is implemented using the Binary Search Tree and some other balanced trees such as RB-tree, AVL-tree and B-tree for both IPV4 and IPV6 prefixes. Comparison results show better lookup and update performance or superior storage requirements for Scalar Prefix Search in both average and worst cases, against current solutions like PIBT [1] and LPFST [2].

Multi-constraint quality-of-service routing will become increasingly important as the Internet evolves to support real-time services. It is well known however, that optimum multi-constraint QoS routing is computationally complex, and for this reason various heuristics have been proposed for routing in practical situations. Among these methods, those that use a single mixed metric are the most popular. Although mixed metric routing discards potentially useful information, this is compensated for by significantly reduced complexity. Exploiting this tradeoff is becoming increasingly important where low complexity designs are desired, such as in battery operated wireless applications. In this paper, a novel single mixed metric multi-constraint routing algorithm is introduced. The proposed technique has similar complexity compared with existing low complexity methods. Simulation results are presented which show that it can obtain better performance than comparable techniques in terms of generating feasible multi-constraint QoS routes.

In this paper, the problem of random access contention between machine type devices (MTDs) in the uplink of a wireless cellular network is studied. In particular, the possibility of forming cooperative groups to coordinate the MTDs' requests for the random access channel (RACH) is analyzed. The problem is formulated as a stochastic coalition formation game in which the MTDs are the players that seek to form cooperative coalitions to optimize a utility function that captures each MTD's energy consumption and time-varying queue length. Within each coalition, an MTD acts as a coalition head that sends the access requests of the coalition members over the RACH. One key feature of this game is its ability to cope with stochastic environments in which the arrival requests of MTDs and the packet success rate over RACH are dynamically time-varying. The proposed stochastic coalitional is composed of multiple stages, each of which corresponds to a coalitional game in stochastic characteristic form that is played by the MTDs at each time step. To solve this game, a novel distributed coalition formation algorithm is proposed and shown to converge to a stable MTD partition. Simulation results show that, on the average, the proposed stochastic coalition formation algorithm can reduce the average fail ratio and energy consumption of up to 36% and 31% for a cluster-based distribution of MTDs, respectively, compared to a noncooperative case. Moreover, when the MTDs are more sensitive to the energy consumption (queue length), the coalitions' size will increase (decrease).