Turkmen Canli

CL-MAC: A Cross-Layer MAC protocol for heterogeneous Wireless Sensor Networks

In duty cycled MAC protocols, multi-packet, multi-flow and multi-hop traffic patterns experience significant latencies, which are partially due to duty cycling. Several cross-layer routing/MAC schemes have been proposed to mitigate this latency. However, they utilize routing information from a single flow and/or a single packet perspective, thus limiting their adaptation to varying traffic loads and patterns. In this paper, we propose a novel Cross-Layer MAC protocol (CL-MAC) for WSNs, to efficiently handle multi-packet, multi-hop and multi-flow traffic patterns while adapting to a wide range of traffic loads. CL-MACs scheduling is based on a unique structure of flow setup packets that efficiently utilize routing information to transmit multiple data packets over multiple multi-hop flows. Unlike other MAC protocols, supporting construction of multi-hop flows, CL-MAC considers all pending packets in the routing layer buffer and all flow setup requests from neighbors, when setting up a flow. This allows CL-MAC to make more informed scheduling decisions, reflecting the current network status, and dynamically optimize its scheduling mechanism accordingly. We evaluate CL-MAC through extensive ns-2 simulations and compare its performance to the state of the art, over various networks and for a wide variety of traffic loads and patterns. In all our experiments, CL-MAC substantially reduces end-to-end latency, increases delivery ratio while reducing the average energy consumed per packet delivered.

PRMAC: Pipelined Routing Enhanced MAC Protocol for Wireless Sensor Networks

We describe a novel pipelined routing enhanced MAC protocol (PRMAC) for wireless sensor networks that employs cross layer optimization to realize efficient channel access over multiple hops. The main goal of the protocol is to schedule multi hop transmissions of data packets in a single cycle to reduce end-to-end delay. When nodes are awake, a multi-hop flow is constructed using the information from the routing layer, and data packet transmission is scheduled in the subsequent sleep period. When compared with existing solutions such as RMAC, our ns-2 based simulation results show an average 62.2% reduction in end to end packet delivery time for unicast traffic on random networks and up to 200% improvement in delivery ratio on multi-hop chains.

BulkMAC: a cross-layer based MAC protocol for wireless sensor networks

This paper presents a duty cycling based cross layer MAC protocol for wireless sensor networks (WSNs), referred to as BulkMAC, to support the transmission of multihop multiple packet flows during a single sleep period. We show that without the proposed cross-layered approach, the sensor nodes will spend significant energy and induce longer delays. The proposed protocol cleverly schedules the channel allocation using the upper routing layer information. We implement our protocol in ns2.29 and compare it against RMAC (Routing Enhanced MAC Protocol) and PRMAC (Pipelined-RMAC). On the average, BulkMAC improves data delivery by a factor of 2.26 and 1.67 compared to RMAC and PRMAC, respectively, for data collection in random networks.

A Cross-Layer Optimization Approach for Efficient Data Gathering in Wireless Sensor Networks

Data collection techniques in wireless sensor networks (WSN) suffer from heavy congestion particularly at nodes closer to the sink node. In order to combat this problem, either complex MAC layer protocols have been proposed or non scalable data collection solutions have been designed. We propose a novel cross layer optimization approach that assumes a very simple MAC protocol and makes use of both routing and MAC layers information to reduce congestion, improve delivery ratio, and optimize energy usage. The proposed approach uses multiple disjoint collection trees, rooted from sink, with non overlapping duty cycles. At the MAC layer, we exploit the fact that nodes that are on different data collection trees need not to communicate with each other, hence the SMAC based wake up and sleep schedule for each tree is different. Existing multiple tree based data collection protocols have been designed primarily for fault tolerance or load balancing. For MAC layer part of our protocol, we have modified the SMAC code available in ns-2.28 to simulate our data collection scheme. Our scheme improves the data delivery ratio up to 40% for regular traffic, and reduces energy consumption by 30%.

Power Efficient Algorithms for Computing Fast Fourier Transform over Wireless Sensor Networks

Collaborative signal processing is one of the most promising applications that are currently being investigated for sensor networks. In this paper, we use FFT computation as a vehicle to highlight the issues involved in realizing distributed computations over sensor networks that have global and local communication and synchronization characteristics. We present a power efficient algorithm for computing 1-D Fast Fourier Transform (FFT) over single and multi-hop wireless sensor networks. The proposed algorithm reduces the number of transmissions, eliminates typical redundant computations in a distributed FFT algorithm and uniformly maps complex multiplications over all the sensors nodes by introducing an extra bit-complement permutation stage after first (log2N)/2 iterations. We show that the proposed algorithm improves energy consumption by 36% on the average on multi-hop sensor networks. This saving in energy consumption significantly improves the battery life of the sensor nodes thereby increasing lifetime of the sensor network.

Context modeling in collaborative sensor network applications

The design of context aware protocols in Wireless Sensor Networks (WSNs) is an emerging challenge. The interpretation of the sensed information greatly depends on the context and for efficient processing and communication, context awareness can play a major role. In most of the existing WSN protocols, context awareness is exploited in a single dimension and is captured either at the application layer or at the routing layer using a single context parameter. In this paper, we develop a new WSN context model to efficiently capture multiple context parameters in multiple dimensions (i.e. context from/to different layers of the network stack) and adjust the network behavior accordingly while simultaneously balancing the network load. The new model not only considers context parameters reflecting run-time application demands from a node, but also takes into consideration the current state of the node as well as the state and demands of neighboring nodes (inter-nodal context sharing). The new model: (a) represents context demands from each layer; (b) reflects the current individual state of each layer; (c) communicates (a) and (b) to the neighboring nodes to impact the decision process; and (d) locally distributes available resources aiming to achieve an optimal load balance. We show the application of this model in realizing a cross-layer optimized protocol for routing multi-hop and multi-packet traffic in WSNs.

Supporting multi-hop and multi-packet transmission in asynchronous WSNs

Cross-layer design approaches explore the benefits of information exchange between different layers of the network stack. In this paper, we present a cross-layer design approach to support multi-hop and multi-packet routing in asynchronous networks. This is achieved by realizing implicit temporary synchronization at the MAC layer while using multi-hop, multi-packet routing information, and current duty-cycling information. We formally present the cost of information sharing and maintenance at different levels of granularity across network layers and among neighboring nodes. The benefits of information sharing and communication are tremendous. The proposed cross-layer approach can be applied to any asynchronous MAC scheme. For example, when applied to RI-MAC (one of the most efficient and recent asynchronous MAC protocols), it reduces its latency by 34% and improves power consumption by 32% on a 16-node clique network, without compromising throughput. The proposed approach was also tested on a 10-node chain and a 25-node cross chain at various traffic loads and flows; it showed great improvements in latency without affecting power consumption or delivery ratio.

Power - time optimal algorithm for computing FFT over sensor networks

Deployment of a vast array of tiny smart sensors (sensor devices with processors) interconnected over wireless channels are enabling their pervasive use in a variety of defense and commercial applications, such as environmental monitoring (e.g. traffic, habitat, security), industrial sensing and diagnostics (e.g. factory, appliances), infrastructures (e.g. power grid, water distributions, waste disposal), and battlefield awareness (e.g. multi-target tracking). While the task of developing and implementing pervasive applications for such a scenario is exciting, it poses tremendous challenges. In this context, design of algorithms for processing information in sensor networks is an emerging research area. However, due to the unique characteristics of the sensor network computing paradigm, the objective is to design algorithms that meet the two contradicting goals of low power and fast executions times, i.e., the algorithms should be energy efficient and they should exploit the parallel computing resources available in sensor nodes. In this paper, we present a power-time efficient algorithm for computing Fast Fourier Transform over data distributed across smart sensors. The Fast Fourier Transform (FFT) [2] has been studied extensively as a frequency analysis tool in diverse application areas such as audio, signal, and image processing, and several other real time data applications [5].