CSMA/CA is a well known medium access mechanism extensively used in wireless networks. By detecting the carrier sensing (CS) signal, nodes determine whether the status of the wireless medium is busy or idle. However, recent works have shown that besides detecting the channel status, these signals can be used to derive the transmitted packet size at the nodes in the CS range. In this paper, we present the feasibility of this technique using CC2420 radio. In addition, we show how we can apply larger CS range and packet size detection to solve well-known problems such as reducing latency in the wireless sensor network (WSN). To our knowledge, the proposed solution is the first trial that applies such techniques to design the delay-sensitive scheduling for WSN. Based on our ns-2 simulation, we show that our proposal reduces latency significantly compared to the existing listen/sleep scheduling based protocols.

Because of the fact that mobile communication channel changes by time, it is necessary to employ adaptive channel equalizers in order to combat the distorting effects of the channel. Least Mean Squares (LMS) algorithm is one of the most popular channel equalization algorithms and is preferred over other algorithms such as the Recursive Least Squares (RLS) and Maximum Likelihood Sequence Estimation (MLSE) when simplicity is the dominant decision factor. However, LMS algorithm suffers from poor performance and convergence speed within the training period specified by most of the standards. The aim of this study is to improve the convergence speed and performance of the LMS algorithm by adjusting the step size using fuzzy logic. The proposed method is compared with the Channel Matched Filter-Decision Feedback Equalizer (CMF-DFE) [1] which provides multi path propagation diversity by collecting the energy in the channel, Minimum Mean Square Error-Decision Feedback Equalizer (MMSE-DFE) [2] which is one of the most successful equalizers for the data packet transmission, normalized LMS-DFE (N-LMS-DFE) [3] , variable step size (VSS) LMS-DFE [4] , fuzzy LMS-DFE [5],[6] and RLS-DFE [7] . The obtained simulation results using HIPERLAN/1 standards have demonstrated that the proposed LMS-DFE algorithm based on fuzzy logic has considerably better performance than others.

In real-time data-dominated communication and multimedia processing applications, a multi-layer memory hierarchy is typically used to enhance the system performance and also to reduce the energy consumption. Savings of dynamic energy can be obtained by accessing frequently used data from smaller on-chip memories rather than from large background memories. This paper focuses on the reduction of the dynamic energy consumption in the memory subsystem of multidimensional signal processing systems, starting from the high-level algorithmic specification of the application. The paper presents a formal model which identifies those parts of arrays more intensely accessed, taking also into account the relative lifetimes of the signals. Tested on a two-layer memory hierarchy, this model led to savings of dynamic energy from 40% to over 70% relative to the energy used in the case of flat memory designs.

The application of Cumulative Decision Feedback (DF) technique for energy/complexity constrained Wireless Sensor Networks (WSN) is considered. Theoretical bit error probability and average rate of a BPSK modulated DF are derived together with PHY-MAC layers' energy efficiency model for DF and Forward Error Correction (FEC) techniques. Moreover, an empirical optimization, which in turn relies upon a low complexity SNR estimation method also derived in this letter, is applied to the DF technique in order to obtain maximum energy efficiency.

Amidst conflicting views about whether soft cooperative energy detection scheme (SCEDS) outperforms hard cooperative energy detection scheme (HCEDS) greatly in cognitive radio, we establish the bridge that mathematically connects SCEDS and HCEDS by closed approximations. Through this bridge, it is demonstrate that, if the number of detectors of HCEDS is 1.6 times as that of SCEDS, they have nearly the same performance which is confirmed by numerical simulations, enabling a quantitative evaluation of the relation between them and a resolution of the conflicting views.

In real-time embedded systems, there is requirement for adapting both energy consumption and Quality of Services (QoS) of tasks according to their importance. This paper proposes an adaptive power-aware resource allocation method to resolve a trade-off between the energy consumption and QoS levels according to their importance with guaranteeing fairness. The proposed resource allocator consists of two components: the total resource optimizer to search for the optimal total resource and QoS-fairness-based allocator to allocate resource to tasks guaranteeing the fairness. These components adaptively achieve the optimal resource allocation formulated by a nonlinear optimization problem with the time complexity O(n) for the number of tasks n even if tasks' characteristics cannot be identified precisely. The simulation result shows that the rapidness of the convergence of the resource allocation to the optimal one is suitable for real-time systems with large number of tasks.

To fully utilize the limited battery energy of mobile electronic devices, we propose an adaptive adjustment method of processing quality for multiple image stream tasks running with widely varying execution times. This adjustment method completes the worst-case executions of the tasks with a given budget of energy, and maximizes the total reward value of processing quality obtained during their executions by exploiting the probability distribution of task execution times. The proposed method derives the maximum reward value for the tasks being executable with arbitrary processing quality, and near maximum value for the tasks being executable with a finite number of processing qualities. Our evaluation on a prototype system shows that the proposed method achieves larger reward values, by up to 57%, than the previous method.

This paper analyzes the limitations of the multihoming support in the Proxy Mobile IPv6 protocol, then proposes an enhanced multihoming support scheme based on a per-interface address configuration method. The proposed scheme can provide a more flexible multihoming support and also maintain application session continuity during a handoff between two interfaces by using IPv6 extension headers. Plus, flow distribution with filters is also used to realize the advantages of multihoming. Simulation results with the OPNET validate the proposed multihoming support scheme for convergent networks.

In this letter, a decision-directed MOE detector with excellent robustness against signature waveform mismatch is proposed for DS-CDMA systems. Both the theoretic analysis and computer simulation results demonstrate that the proposed detector can provide better SINR performance than that of conventional detectors.

One of the major challenges facing the design of a routing protocol for Wireless Sensor Networks (WSNs) is to find the most reliable path between the source and sink node. Furthermore, a routing protocol for WSN should be well aware of sensor limitations. In this paper, we present an energy efficient, scalable, and distributed node disjoint multipath routing algorithm. The proposed algorithm, the Energy-aware Multipath Routing Algorithm (EMRA), adjusts traffic flows via a novel load balancing scheme. EMRA has a higher average node energy efficiency, lower control overhead, and a shorter average delay than those of well-known previous works. Moreover, since EMRA takes into consideration network reliability, it is useful for delivering data in unreliable environments.

MIMO (Multiple Input Multiple Output) communications systems equipped with array antennas at both the transmitter and receiver sides are a promising scheme to realize higher rate and/or reliable data transmission. In this paper, capacity analysis of MIMO Rayleigh channel with spatial correlation at the receiver of multipath taken into account is presented. In general, a model configuration of local scattering around a mobile station in MIMO environment is carried out by simulation to examine spatial correlation coefficients. Based on statistical properties of the eigenvalues of correlated complex random Wishart matrices, the exact closed-form expressions of distribution of the eigenvalues are investigated. Then, the general closed-form evaluation of integral form is proposed based on Meijer's G-function. The results demonstrate that the ergodic capacities are improved by increasing the number of the antennas and the SNR's. Compared with i.i.d. (independent identically distributed) Rayleigh channel, the incremental improvement of correlated Rayleigh channel is reduced by spatial fading correlation. The analytical results validated by Monte-Carlo simulations show a good agreement.

We propose a medium access control (MAC) protocol for real-time applications in one-hop ad-hoc wireless networks. It is a distributed mechanism that takes account of priority and has a bounded packet delay. Nodes use energy signals to contend for the right to access the channel. Nodes, which have a packet to transmit, send energy signals or listen to the channel based on their binary frame. The node that has sent energy signals and has not heard any energy signals wins the right to access the channel. We use two schemes to determine the binary frame: at the beginning of a session, a node determines it based on its priority level and a random number; after successful transmission, based on a count of successful packet transmissions. With the first scheme, in order to reduce contention losses, the nodes that had won the right to access the channel but failed in transmission have priority over the other nodes. With the second scheme, the node that has the largest count, the one that has been waiting the longest, can send a packet without risking collision. The protocol provides higher probability of successful transmission and a limit on maximum packet delay. An analysis of the protocol provides conditions for the protocol to be stable. We evaluate the performance of the proposed protocol using simulations of a network with a mixed population of data and real-time nodes, whose source is constant bit rate (CBR) and a two state Markov on/off process.

It is observed, surprisingly, that existing nearest neighbor search methods in wireless data broadcast may not work effectively on mobile clients with very limited memory space. To resolve this problem, a novel method for nearest neighbor search is introduced in the context of a representative of indexes, the grid-partition index, in wireless data broadcast. In the proposed scheme, a mobile client performs the nearest neighbor search by making a sequential access to index packets according to their broadcast order over a wireless channel. The performance evaluation demonstrates that our approach substantially outperforms limited memory versions of existing methods in terms of access time, while retaining a good energy conservation.

Wireless sensor network (WSN) is a promising approach for a variety of applications. Routing protocol for WSNs is very challenging because it should be simple, scalable, energy-efficient, and robust to deal with a very large number of nodes, and also self-configurable to node failures and changes of the network topology dynamically. Recently, many researchers have focused on developing hierarchical protocols for WSNs. However, most protocols in the literatures cannot scale well to large sensor networks and difficult to apply in the real applications. In this paper, we propose a novel adaptive routing protocol for WSNs called ARPEES. The main design features of the proposed method are: energy efficiency, dynamic event clustering, and multi-hop relay considering the trade-off relationship between the residual energy available of relay nodes and distance from the relay node to the base station. With a distributed and light overhead traffic approach, we spread energy consumption required for aggregating data and relaying them to different sensor nodes to prolong the lifetime of the whole network. In this method, we consider energy and distance as the parameters in the proposed function to select relay nodes and finally select the optimal path among cluster heads, relay nodes and the base station. The simulation results show that our routing protocol achieves better performance than other previous routing protocols.

This paper presents a new routing approach to extend the effective lifetime of mobile ad hoc networks (MANET) considering both residual battery energy of the participating nodes and routing cost. As the nodes in ad hoc networks are limited in power, a power failure occurs if a node has insufficient remaining energy to send, receive or forward a message. So, it is important to minimize the energy expenditure as well as to balance the remaining battery power among the nodes. Cost effective routing algorithms attempt to minimize the total power needed to transmit a packet which causes a large number of nodes to loose energy quickly and die. On the other hand, lifetime prediction based routing algorithms try to balance the remaining energies among the nodes in the networks and ignore the transmission cost. These approaches extend the lifetime of first few individual nodes. But as nodes spend more energy for packet transfer, power failures occurs within short interval resulting more number of total dead node earlier. This reduces the effective lifetime of the network, as at this stage successful communication is not possible due to the lack of forwarding node. The proposed method keeps the transmission power in modest range and at the same time tries to reduce the variance of the residual energy of the nodes more effectively to obtain the highest useful lifetime of the networks in the long run. Nonetheless, movement of nodes frequently creates network topology changes via link breaks and link creation and thus effects on the stability of the network. So, the pattern of the node movement is also incorporated in our route selection procedure.

In multiple-input multiple-output (MIMO) wireless systems, the receiver must extract each transmitted signal from received signals. Iterative signal detection with belief propagation (BP) can improve the error rate performance, by increasing the number of detection and decoding iterations in MIMO systems. This number of iterations is, however, limited in actual systems because each additional iteration increases latency, receiver size, and so on. This paper proposes a convergence acceleration technique that can achieve better error rate performance with fewer iterations than the conventional iterative signal detection. Since the Log-Likelihood Ratio (LLR) of one bit propagates to all other bits with BP, improving some LLRs improves overall decoder performance. In our proposal, all the coded bits are divided into groups and only one group is detected in each iterative signal detection whereas in the conventional approach, each iterative signal detection run processes all coded bits, simultaneously. Our proposal increases the frequency of initial LLR update by increasing the number of iterative signal detections and decreasing the number of coded bits that the receiver detects in one iterative signal detection. Computer simulations show that our proposal achieves better error rate performance with fewer detection and decoding iterations than the conventional approach.

In this paper, we design an adaptive mobility-supporting MAC (AM-MAC) protocol for mobile sensor networks. In our protocol, as in S-MAC [1], each virtual cluster that consists of a subset of sensor nodes has its own listen-sleep schedule. Hence, when a mobile sensor node moves into a new virtual cluster, it needs to adapt to the listen-sleep schedule of the corresponding virtual cluster in a timely and energy efficient manner. To this end, in our protocol, we utilize schedule information on border nodes between virtual clusters. This allows us to implement fast and energy efficient listen-sleep schedule adaptation for mobile nodes, which consists of two main functions: energy efficient secondary listen period and smart scheduling adaptation. Simulation results show that our protocol can provide fast schedule adaptation while achieving energy efficiency.

To conserve energy, periodic active/sleep dynamics is adopted in wireless sensor networks. At the same time, the QoS guarantees, such as packet delay, packet loss ratio and network throughput need to be satisfied. We develop a finite queuing model for sensor nodes and derive network performance for contention-based wireless sensor networks with synchronous wakeup patterns. Furthermore, the impact of active/sleep duty cycle, time scale and node buffer size on the tradeoff between energy efficiency and QoS guarantees is studied based on the model. Simulation results well match our analytical results and validate the accuracy of our model and approach.

A key issue in QoS-provisioning real-time wireless communications is to provide the QoS requirement with low energy consumption. In this paper, we propose an energy-efficient error correction scheme for real-time communications with QoS requirements in wireless networks. The QoS requirement of a message stream is modeled with (m, k) constraint, implying that at least m messages should be sent to a receiver during any window of k periods. The proposed scheme adaptively selects an error correcting code in an energy-efficient manner so that it maximizes the number of QoS provisionings per unit energy consumption.

In this letter, we propose a new energy efficient online routing algorithm for QoS-sensitive sensor networks. An important design principle underlying our algorithm is online decision making based on real time network estimation. This on-line approach gives adaptability and flexibility to solve a wide range of control tasks for efficient network performance. In addition, our distributed control paradigm is practical for real sensor network management. Simulation results indicate the superior performance of our algorithm between energy efficiency and QoS provisioning.