Genetic sequence identification from electrical characterization of single molecules has emerged as a promising alternative to traditional approaches. Since electrical data on single molecules is extremely noisy due to the limitations of even state-of-the-art approaches, achieving high detection rates is challenging, particularly when the task involves being able to distinguish a sequence from its single base-pair mismatches. To address this issue, we propose an architecture based on combining a convolutional neural network with an ensemble learning method, XGBoost. In addition, four different input feature representations are considered, 1D conductance probability distributions and 2D conductance versus distance probability distributions which can be viewed as images, with or without averaging over the experimental parameters. The with averaging case corresponds to feature matrices derived from mixed datasets. We find that 2D probability distributions are helpful with respect to classifier accuracy, but averaged conductance probability distributions are much more impactful and significantly enhance prediction accuracy. Our quantitative analysis of multiple sequences shows an impressive performance increase of approximately 10% for all sequences. While the basis of our analysis is conductance data of DNA strands and their single base-pair mismatches, our method is generally applicable to other single-molecule identification based on their conductance.

We propose a contention based reservation MAC protocol for a collaborative sensing scenario involving a set of surveillance UAVs communicating with a hub. Data transmission rights are secured via distributed contention access between the UAV nodes for K available mini-slots. Each node chooses a slot with probability p independently of the others and is only allowed one attempt in a frame. We investigate the optimal choice of p which maximizes the one-shot probability of success(or alternately, the expected number of successes), beta, over the K minislots. We show that beta is prone to local maxima and develop an empirical formula based on goodness-of-fit which matches the numerically obtained values closely.

The recent ratification of IEEE 802.15.4 PHY-MAC specifications for low-rate wireless personal area networks represents a significant milestone in promoting deployment of wireless sensor networks (WSNs) for a variety of commercial uses. The 15.4 specifications specifically target wireless networking among low-rate, low-power and low-cost devices that is expected to be a key market segment for a large number of WSN applications. In this article, we first analyze the performance of the contention access period specified in the IEEE 802.15.4 standard in terms of throughput and energy consumption. This analysis is facilitated by a modeling of the contention access period as nonpersistent CSMA with backoff. We show that, in certain applications in which having an inactive period in the superframe may not be desirable due to delay constraints, shutting down the radio between transmissions provides significant savings in power without significantly compromising the throughput. We also propose and analyze the performance of a modification to the specification which could be used for applications in which MAC-level acknowledgements are not used. Extensivens-2 simulations are used to verify the analysis.

The combination of multiple radio nodes in con junction with a suitably structured multi-hop or mesh architecture has the potential to solve some of the key limitations of present day wireless access networks that are based on single-radio nodes. This paper addresses the static channel assignment problem for multi-channel multi-radio static wireless mesh networks. We present four metrics based on which mesh channel assignments can be obtained. In particular, we focus on minimization of the average and maximum collision domain sizes and show that these problems are closely related to problems in combinatorial optimization such as MAX k-CUT and MIN k- PARTITION. We also present heuristic algorithms for solving the channel assignment problems using the above two metrics.

In this paper, we consider the problem of distributed linear parameter estimation in static and dynamic sensor networks. We propose iterative averaging algorithms based on Laplacian dynamics which converge to the centralized least squares solution asymptotically. In the first part of this paper, we consider the case of unclustered (flat architecture) sensor networks and analyze convergence of the iterative algorithm, for both static and dynamic topologies. Subsequently, we extend our analysis to static but clustered sensor networks with pulsed inter-cluster updates. In this scheme, we assume that all inter-cluster communications occur every H time steps, H > 1, and the corresponding updates are held till the next update instant. Depending on the sensor locations and the topology formation algorithm used, it may be the case that inter-cluster communications require higher transmitter power support than intra-cluster communications. From a power efficiency (or alternately, network lifetime) point of view, it may therefore be beneficial to limit the extent of inter-cluster communication, without significantly enhancing the convergence time of the distributed estimation algorithm. We anticipate that a pulsed inter-cluster update scheme will also be beneficial for applications such as military sensor networks, where low probability of detection and interception is essential. Our analysis provides sufficient conditions under which the distributed algorithm operating on a pulsed inter-cluster update scheme converges. Simulation results are provided which illustrate the dependence of the convergence rate of the algorithm on H

We consider the problem of survivable minimum power bidirectional topology optimization in wireless networks. Two optimization problems are considered, minimization of the total transmit power and minimization of the maximum transmit power, subject to arbitrary K-node connectivity constraints. In this paper, we take an algebraic view of graph connectivity which is defined as the second smallest eigenvalue of the Laplacian matrix of a graph. Since the algebraic connectivity of a graph is known to be a lower bound on its node and edge connectivities, we impose a constraint on the algebraic connectivity to ensure that the solution is K-connected. Given the special properties of the Laplacian matrix of a graph, it is possible to express the algebraic connectivity requirement as a positive semidefinite constraint. Using this result, we show how the optimization problems can be formulated as 0/1 semidefinite programs (SDP's), which can be solved exactly using an SDP solver within a branch-and-bound framework. Linear and semidefinite relaxations of the 0/1 constraints are also discussed. These relaxations are useful for generating lower bounds on the exact 0/1 SDP models.

We consider the problem of power controlled minimum frame length scheduling for TDMA wireless networks. Given a set of one-hop transmission requests, our objective is to schedule them in a minimum number of time slots, so that each slot schedule is free of self-interferences and meets desired SINR constraints. Additionally, the transmit power vector corresponding to each slot schedule should be minimal. We consider two different versions of the problem, a per-slot version and a per-frame version, and develop mixed integer linear programming models which can be used for solving the problems optimally. In addition, we propose a heuristic algorithm for the per-slot version.

Abstract — The combination of multiple radio nodes in conjunction with a suitably structured multi-hop or mesh architecture has the potential to solve some of the key limitations of present day wireless access networks that are based on single-radio nodes. This paper addresses the channel assignment problem for multi-channel multi-interface (radio) wireless mesh networks. We focus on static wireless mesh networks where multiple nonoverlapping channels are available for each wireless interface. In this network environment, our objective is to find a fixed channel assignment which maximizes the number of bidirectional links that can be activated simultaneously, subject to interference constraints. We present two mixed integer linear programming models for solving the fixed channel assignment problem with multiple radios. Detailed computational results on various grid topologies are also presented and discussed. I.

We consider the "at least" version of the generalized minimum spanning tree problem (L-GMST). Unlike the MST, the L-GMST is known to be NP-hard. In this paper, we propose an ant colony system based solution approach for the L-GMST. A key feature of our algorithm is its use of ants of different behavioral characteristics, which are adapted over time. Computational results on datasets used in earlier literature indicate that our algorithm provides similar or better results for most of them.

Broadcasting in wireless networks, unlike wired networks, inherently reaches several nodes with a single transmission. For an omnidirectional wireless broadcast to a node, all nodes closer to the transmitting node are also reached. This property can be used to compute routing trees which minimize the sum of the transmitter powers. We present a mixed integer programming formulation and a simulated annealing algorithm for the problem. Extensive experimental results for the heuristic approach are presented. They show that the proposed algorithm is capable of improving the results of state-of-the-art algorithms for most of the problems considered. The solutions provided by the simulated annealing algorithm can be improved by applying a very fast post-optimization procedure. This leads to the best known mean results for the problems considered.

We consider the problem of minimum power bidirectional topology optimization in wireless networks with sectored antennas. We first develop a mixed integer linear programming model for optimal solution of the problem with sectored antennas. Subsequently, we discuss a centralized heuristic algorithm which is based on Kruskal's algorithm for the minimum spanning tree problem. However, unlike Kruskal's algorithm which chooses minimum cost edges from a set of edge weights, our algorithm uses an incremental cost mechanism to select edges. This incremental cost mechanism is motivated by the inherently broadcast nature of the wireless medium. We also discuss a simple branch exchange heuristic which can be used to improve the topology generated by the Kruskal-like algorithm. Simulation results indicate that reasonably good solutions can be obtained using the proposed heuristic algorithms.

In this paper the problem of minimum power multicasting in wireless networks with sectored antennas is considered. For omnidirectional antenna systems, a transmission from node i to node j will also reach all nodes which are closer to i than j. Depending on the network geometry, this strategy can be highly power efficient In ideal sectored antenna systems, however, this phenomenon is sector specific; i.e., only those nodes which are located in the same sector as j will receive the transmission implicitly. Though this might seem an apparent disadvantage, the higher gains associated with directional antennas (as opposed to omni-directional antennas) allow for reduced transmission powers without sacrificing the signal-to-interference ratio at the receiver. In this paper, we first develop a mixed integer linear programming model for optimal solution of the minimum power multicast problem with sectored antennas. Subsequently, we discuss a biologically inspired algorithm for solving the problem to near-optimality at a very reasonable computation time. Experiments on randomly generated 10, 20 and 30-node networks indicate that near-optimal solutions can be obtained using the proposed algorithm.

We consider the problem of maximizing the time-to-first-failure, defined as the time till the first node in the network runs out of battery energy, in energy constrained broadcast wireless networks. We discuss a greedy algorithm and prove that it solves the problem optimally for a broadcast application, in polynomial time, provided the complete power matrix and the battery residual capacities are known.

Broadcasting in wireless networks, unlike wired networks, inherently reaches several nodes with a single transmission. For omni-directional wireless broadcast to a node, all nodes closer will also be reached. This property can be used to compute routing trees which minimize the sum of the transmitter powers. It has been shown that this problem is NP-complete. In this paper, we present the r-shrink procedure, a heuristic for improving the solutions obtained using fast sub-optimal algorithms. Specifically, we focus on the low-complexity BIP algorithm and Prim's minimum spanning tree algorithm and show through extensive simulations that better solutions are obtained almost always, with considerably lower tree power, if the proposed procedure is used to improve the trees generated using these algorithms.

Wireless multicast/broadcast sessions, unlike wired networks, inherently reach several nodes with a single transmission. For omnidirectional wireless broadcast to a node, all nodes closer will also be reached. Heuristic algorithms for constructing the minimum power tree in wireless networks have been proposed by Wieselthier et al. and Stojmenovic et al. Recently, an evolutionary search procedure has been proposed by Marks et al. In this paper, we present three different integer programming models which can be used for an optimal solution of the minimum power broadcast/multicast problem in wireless networks. The models assume complete knowledge of the distance matrix and is therefore most suited for networks where the locations of the nodes are fixed.