We present Texas Instruments' implementation of IEEE 802.15.4e protocols (CSL and TSCH) in a multi-hop mesh. At the MAC layer, we discuss techniques which reduce radio duty-cycle in our CSL implementation. Further, we propose RPL adaptation, to operate the network in 2 phases, one with small DIO timer and another with large DIO timer to minimize the impact of RPL broadcasts. Our demo shows 10x reduction in radio power consumption resulting from careful implementation of our MAC and Routing layer techniques using TelosB motes running Contiki OS.

Sensor networks are increasingly available in home and industry automation applications. The use of battery-powered wireless sensor nodes eases network deployment, but the dynamic wireless medium, along with energy and bandwidth constraints, poses a great challenge to achieve a robust sensor network only with these nodes.

Traditionally, network buffer resources have been used at routers to queue transient packets to prevent packet drops. In contrast, we propose a scheme for large multihop networks where intermediate routers have no buffers for queueing transient packets. In the proposed scheme, network storage resources (memory) are used only at source and destination nodes to encode/decode packets using random linear coding over time. Our scheme capitalizes on the common empirical observation that for large networks with many flows through each router, if packet loss occurs in a flow path, it will very likely occur only at only a very few links on the path. Unfortunately, the location of this congested link varies with time, thereby preventing prevailing static buffer allocation strategies from exploiting this observation. We propose source coding over packets at the session layer as a means of “sharing” memory across links along a flow path. We call this spatial buffer multiplexing-where buffering and coding implemented at the source compensates for packet loss at any downstream bufferless link. First, we consider large spatial multihop networks withNnodes (each with finite buffer space for coding/decoding) and Θ(N) flows; the number of flows through each link scales as Ω(Nα) for some α ∈ (0,1). Using many-sources large deviations analysis, we show that to obtain comparable packet drop probabilities (QoS), spatial buffer multiplexing provides an order-wise buffer gain of Ω(Nα) per node over traditional static buffer allocation for queueing. Next we consider the complementary case of a network with a small number of flows through a buffer, but large source buffer. Here, we provide a sufficient condition under which the packet loss probability decreases exponentially in a function that is linear in the size of the input buffer, where the function is required to meet a predetermined negative slope -δ. We express the loss effective bandwidth for coding and compare it against that for queueing.

This correspondence derives bounds on the jamming capacity of a slotted ALOHA system. A system with n legitimate users, each with a Bernoulli arrival process is considered. Packets are temporarily stored at the corresponding user queues, and a slotted ALOHA strategy is used for packet transmissions over the shared channel. The scenario considered is that of a pair ofillegitimateusers that jam legitimate transmissions in order to communicate over the slotted ALOHA channel. Jamming leads to binary signaling between the illegitimate users, with packet collisions due to legitimate users treated as (multiplicative) noise in this channel. Further, the queueing dynamics at the legitimate users stochastically couples the jamming strategy used by the illegitimate users and the channel evolution. By considering various independent and identically distributed (i.i.d.) jamming strategies, achievable jamming rates over the slotted ALOHA channel are derived. Further, an upper bound on the jamming capacity over the class of all ergodic jamming policies is derived. These bounds are shown to be tight in the limit where the offered system load approaches unity.

We consider a capacity planning optimization problem in a general theoretical framework that extends the classical Erlang loss modeland related stochastic loss networks to support time-varying workloads. The time horizon consists of a sequence of coarse time intervals, each of which involves a stochastic loss network under a fixed multi-class workload that can change in a general manner from one interval to the next. The optimization problem consists of determining the capacities for each time interval that maximize a utility function over the entire time horizon, finite or infinite, where rewards gained from servicing customers are offset by penalties associated with deploying capacities in an interval and with changing capacities among intervals. We derive a state-dependent optimal policy within the context of a particular limiting regime of the optimization problem, and we prove this solution to be a symptotically optimal. Then, under fairly mild conditions, we prove that a similar structural property holds for the optimal solution of the original stochastic optimization problem, and we show how the optimal capacities comprising this solution can be efficiently computed.

Traditionally, network buffer resources have been used at routers to queue transient packets to prevent packet drops. In contrast, we propose a scheme for large multi-hop networks where intermediate routers have no buffers for queueing transient packets. In the proposed scheme, network storage resources (memory) are used only at source and destination nodes to encode/decode packets using random linear coding over time. Our scheme utilizes the observation that for large networks with many flows through each router, if packet loss occurs in a flow path, it will very likely occur only at only one link in the path. Unfortunately, the location of this congested link varies with time, hence, preventing static buffer allocation strategies from exploiting this observation. We propose network coding as a means of “sharing” memory across links along a flow path. We call this spatial buffer multiplexing – where buffering and coding implemented at the source compensates for packet loss at any downstream bufferless link. In this paper, we consider large spatial multi-hop networks with N nodes and Θ(N) flows, where the number of flows through each link scales as Ω(N) for some α ∈ (0, 1). Using many-sources large deviations analysis, we show that to obtain comparable packet drop probabilities (QoS), spatial buffer multiplexing provides an order-wise buffer gain of Ω(N) per node over traditional queueing.

We consider a finite-field model for the wireless broadcast and additive interference network (WBAIN), both in the presence and absence of fading. We show that the single-source unicast capacity (with extension to multicast) of a WBAIN with or without fading can be upper bounded by the capacity of an equivalent broadcast erasure network. We further present a coding strategy for WBAINs with i.i.d. and uniform fading based on random linear coding at each node that achieves a rate differing from the upper bound by no more than O(1/q), where q is the field size. Using these results, we show that channel fading in conjunction with network coding can lead to large gains in the unicast (multicast) capacity as compared to no fading

In this paper we compare the following two methods of transmit preceding for the multiple antenna broadcast channel: vector perturbation applied to channel inversion (also termed zero forcing or ZF) precoding and scalar Tomlinson-Harashima (TH) precoding applied to sum-rate achieving transmit precoding. Our results indicate that vector perturbation applied to channel inversion preceding can significantly reduce power enhancement and yields the full diversity afforded by the channel to each user. Scalar TH-modulo reduction significantly reduces the power enhancement for precoding based on sum-rate criterion. The solution to vector perturbation applied to ZF precoding requires the solution to an integer optimization problem which is exponentially complex, or an approximation to the integer optimization problem which requires the Lenstra-Lenstra-Lovasz algorithm of polynomial complexity. Instead we propose a simpler solution (an approximation) to the vector perturbation problem based on the Rayleigh-Ritz theorem (R.A. Horn and C.R. Johnson, 1985). This approximate solution achieves the same diversity order as the optimal vector perturbation technique, but suffers a small coding loss. This solution is of polynomial complexity order. Further, a small increase in complexity with a "sphere"-based search around this solution yields significantly better performance. Since this vector perturbation is required to be done at the symbol rate, the lower complexity of the proposed algorithm is valuable in practice

The single-source min-cost multicast problem, which can be framed as a convex optimization problem with the use of network codes and convex increasing edge costs is considered. A decentralized approach to this problem is presented by Lun, Ratnakar for the case where all users cooperate to reach the global minimum. Further, the cost for the scenario where each of the multicast receivers greedily routes its flows is analyzed and the existence of a Nash equilibrium is proved. An allocation rule by which edge cost at each edge is allocated to flows through that edge is presented. We prove that under our pricing rule, the flow cost at user equilibrium is the same as the min-cost. This leads to the construction of a selfish flow-steering algorithm for each receiver, which is also globally optimal. Further, the algorithm is extended for completely distributed flow adaptation at nodes in the network to achieve globally minimal cost in steady state. Analogous results are also presented for the case of multiple multicast sessions