Based on a stochastic geometry framework, we establish an analysis of the multi-hop spatial reuse aloha protocol (MSR-Aloha) in ad hoc networks. We compare MSR-Aloha to a simple routing strategy, where a node selects the next relay of the treated packet as to be its nearest receiver with a forward progress toward the final destination (NFP). In addition, performance gains achieved by employing adaptive antenna array systems are quantified in this paper. We derive a tight upper bound on the spatial density of progress of MSR-Aloha. Our analytical results demonstrate that the spatial density of progress scales as the square root of the density of users, and the optimal contention density (that maximizes the spatial density of progress) is independent of the density of users. These two facts are consistent with the observations of , established through an analytical lower bound and through simulations.
The idea of designing routing protocols with the notion of progress is first introduced in @cite_3 , where the authors propose the most forward within radius (MFR) routing. In MFR routing, an emitter selects its next relay, among the nodes within some given range from it, to be the nearest receiver to the destination. @cite_2 propose the more sophisticated selection rule of the MSR-Aloha scheme described above. However, in their analytical framework, they find this selection rule to be difficult to manipulate, and so they apply some modifications to it. These modifications will be discussed and compared to our framework in the next sections. In @cite_9 , the authors propose and analyze the longest edge routing (LER), which applies a similar selection rule as MSR-Aloha. MSR-Aloha and LER differ in one key aspect that is that LER does not consider the direction of the intended destination, which is a challenging analytical aspect. It should be noted that all works cited do not consider the use of adaptive antenna array systems, which is one of the new aspects proposed in this work. Finally, practical implementation issues and complete simulation packages of MSR-Aloha routing are considered and detailed in @cite_2 @cite_0 .
@cite_0 @cite_9 @cite_3 @cite_2
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The multihop spatial reuse Aloha (MSR-Aloha) protocol was recently introduced by Baccelli et aL, where each transmitter selects the receiver among its feasible next hops that maximizes the forward progress of the head of line packet towards its final destination. They identify the optimal medium access probability (MAP) that maximizes the spatial density of progress, defined as the product of the spatial intensity of attempted transmissions times the average per-hop progress of each packet towards its destination. We propose a variant called longest edge routing where each transmitter selects its longest feasible edge, and then identifies a packet in its backlog whose next hop is the associated receiver. The main contribution of this work (and of Baccelli et aL) is the use of stochastic geometry to identify the optimal MAP and the corresponding optimal spatial density of progress. We evaluate the end-to-end delay of a multihop transmission scheme that includes a source, a number of relays, and a destination, in the presence of interferers located according to a Poisson point process. The medium access control (MAC) protocol considered is a combination of TDMA and ALOHA, according to which nodes located a certain number of hops apart are allowed to transmit with a certain probability. Based on an independent transmissions assumption, which decouples the queue evolutions, our analysis provides explicit expressions for the mean end-to-end delay and throughput, as well as scaling laws when the interferer density grows to infinity. If the source always has packets to transmit, we find that full spatial reuse, i.e., ALOHA, is asymptotically delay-optimal, but requires more hops than a TDMA-ALOHA protocol. The results of our analysis have applications in delay-minimizing joint MAC routing algorithms for networks with randomly located nodes.We simulate a network where sources and relays form a Poisson point process, and each source assembles a route to its destination by selecting the relays closest to the optimal locations. We assess both theoretically and via simulation the sensitivity of the end-to-end delay with respect to imperfect relay placements and route crossings. This paper is meant to be an illustration of the use of stochastic geometry for analyzing the performance of routing in large wireless ad hoc (mobile or mesh) networks. In classical routing strategies used in such networks, packets are transmitted on a pre-defined route that is usually obtained by a shortest-path routing protocol. In this paper we review some recent ideas concerning a new routing technique which is opportunistic in the sense that each packet at each hop on its (specific) route from an origin to a destination takes advantage of the actual pattern of nodes that captured its recent (re)transmission in order to choose the next relay. The paper focuses both on the distributed algorithms allowing such a routing technique to work and on the evaluation of the gain in performance it brings compared to classical mechanisms. On the algorithmic side, we show that it is possible to implement this opportunistic technique in such a way that the current transmitter of a given packet does not need to know its next relay a priori, but the nodes that capture this transmission (if any) perform a self-selection procedure to choose the packet relay node and acknowledge the transmitter. We also show that this routing technique works well with various medium access protocols (such as Aloha, CSMA, TDMA). Finally, we show that the above relay self-selection procedure can be optimized in the sense that it is the node that optimizes some given utility criterion (e.g. minimize the remaining distance to the final destination), which is chosen as the relay. The performance evaluation part is based on stochastic geometry and combines simulation as analytical models. The main result is that such opportunistic schemes very significantly outperform classical routing schemes when properly optimized and provided at least a small number of nodes in the network know their geographical positions exactly. Supporting massive device transmission is challenging in machine-to-machine (M2M) communications. Particularly, in event-driven M2M communications, a large number of devices become activated within a short period of time, which in turn causes high radio congestions and severe access delay. To address this issue, we propose a Fast Adaptive S-ALOHA (FASA) scheme for random access control of M2M communication systems with bursty traffic. Instead of the observation in a single slot, the statistics of consecutive idle and collision slots are used in FASA to accelerate the tracking process of network status that is critical for optimizing S-ALOHA systems. With a design based on drift analysis, the estimate of the number of the active devices under FASA converges fast to the true value. Furthermore, by examining the T-slot drifts, we prove that the proposed FASA scheme is stable as long as the average arrival rate is smaller than e-1, in the sense that the Markov chain derived from the scheme is geometrically ergodic. Simulation results demonstrate that under highly bursty traffic, the proposed FASA scheme outperforms traditional additive schemes such as PB-ALOHA and achieves near-optimal performance in reducing access delays. Moreover, compared to multiplicative schemes, FASA shows its robustness under heavy traffic load in addition to better delay performance.