In distributed detection (DD), sensors typically report their sensed information to a fusion center (FC) either in its raw form or in a compressed form, where a final decision is made regarding the true hypothesis generating the data. In this paper, we focus on the scenario where the sensor reporting process is power-constrained, and the reporting channels are corrupted by additive noise. We propose an observation-transformed DD (TDD) framework, where the sensed data are transformed before being reported to the FC using the same power as the DD system without any observation transform. We demonstrate that the Bhattacharyya distance between the distributions of received signals under different hypotheses can be increased with proper design of the transforms. We then show that finding the optimal transform leads to a functional optimization problem, which can be solved using sequential quadratic programming (SQP). Suboptimal transforms are presented for maximizing the Bhattacharyya distance, as well as for maximizing the Kullback-Leibler divergence, and minimizing the Bayes error probability of detection for a single-sensor scenario. The results demonstrate that the proposed framework enhances detection performance and provides insights into the fundamental limits of power-constrained distributed detection in noisy environments.

In conventional codeword-based distributed detection (CDD), sensors quantize their observations and report codewords to the fusion center (FC) where a final decision is made regarding the truthfulness of the hypotheses. Recently, quantized-but-uncoded DD (QDD) has been proposed, where sensors, after quantization, transmit summarized values instead of codewords to the FC. QDD can adapt well to the power constraint and offers better detection performance than CDD. However, the added degree of freedom in parameter selection in QDD comes with high complexity in optimal system design. The contribution of this letter is a proof showing that in QDD, the optimal sensor decision rules for binary decisions are likelihood-ratio-quantizers (LRQ), regardless of the reporting channel conditions, provided that the sensor observations are conditionally independent given the hypotheses. This property largely simplifies the design of QDD. Performance comparison is presented for CDD, QDD, and a benchmark system that reports original sensor observations, when both sensing and reporting channel noise exist.

Performance analysis of the Bundle Protocol (BP) in Delay/ disruption-Tolerant Networks (DTN) has gained much interest. The analytic work of the average operation time (AOT) in BP, for different channel conditions and system parameters, though critical to understanding performance and designing system, is still incomplete. Existing results, such as the average file delivery time (AFDT), provide reasonably close estimates in some cases but exhibit large discrepancies in other cases. In this paper, we provide a holistic probabilistic analysis of the BP process, particularly in the number of error bundles and the position of the last error bundle in each and the last rounds of transmission. We provide, for the first time, an exact solution for the AOT of the BP process. The derivation works for both symmetric and asymmetric channels and is in general for any given maximum number of transmissions rounds. When the unlimited number of transmission rounds is considered, AOT becomes AFDT. The results are further utilized to select the optimal bundle size that gives the minimum file delivery time. Validation via simulations and comparison with existing work are also provided.

A generalized cooperative spectrum sharing (GCSS) scheme for machine-to-machine (M2M) communications is pro- posed in internet-of-things (IoT) systems. The proposed scheme makes use of the existence of massive connected machines to overcome the challenges of spectrum scarcity while avoiding interference and meeting the green requirements of IoT systems. The cooperative proposed scheme extends the coverage of M2M wireless network as well as increasing the throughput while reducing the energy consumption of the connected low power devices. The performance of the GCSS scheme is evaluated analytically by the outage performance by deriving the outage probability. Furthermore, a numerical simulations are presented to support the theoretical findings.

IEEE 802.11 protocol uses random frequency channel assignment which can severely degrade the network throughput. This paper presents a novel efficient throughput oriented frequency channel assignment mechanism with low complexity for the infra-structured IEEE 802.11 based WiFi networks. The core idea of this assignment mechanism is to choose the frequency channel that is just good enough to maximize the number of served users per access point (AP) and leave the over-qualified frequency channels to other APs that may be in need for using them. Unlike other proposed works which aim to solve this IEEE 802.11 problem while ignoring some important network factors (e.g. out- of-system interference, the random AP deployment in reality, the APs not owned by one administrator, etc.), the proposed assignment mechanism takes these factors in consideration. The proposed mechanism performance is compared with the Random, the Greedy and the CACAO channel assignment mechanisms. The results show a remarkable throughput and fairness improvement of the proposed scheme while reserving the simplicity and realism.

In this letter, we consider the effect of noise correlation on the error performance of binary hypothesis signal detection, when one of two deterministic signals is received in correlated Gaussian noise. For the likelihood ratio detection scheme, analytical performance results are derived for equicorrelated and autoregressive order one models. Although it is known previously that the best signal lies in the direction of eigenvector corresponding to the minimum eigenvalue of the noise covariance matrix, our investigation of the variation of mean signal-to-noise power ratio as a function of correlation parameter (i) shows how correlation leads to increased probability of error up to a point, beyond which monotonic decrease in error probability with increasing correlation is possible and (ii) provides a max-min signal design solution for the unknown correlation parameter case. Numerical results are also included for some specific signals.

The Session Initiation Protocol (SIP) is an important multimedia session establishment protocol used on the Internet. Due to the nature and deployment realities of the protocol (ASCII message representation, most deployments over UDP, limited use of message encryption), it becomes relatively easy to attack the protocol at the message level. To mitigate this, self-learning systems have been proposed to counteract new threats. However the efficacy of existing machine learning algorithms must be studied on varied data sets before they can be successfully used. Existing literature indicates that Euclidean distance based classifiers work well to detect anomalous messages. Our work suggests that such classifiers do not produce adequate results for well-crafted malicious messages that differ very slightly from normal messages. To demonstrate this, we gather SIP traffic and minimally perturb it using 13 generic transforms to create malicious SIP messages. We use the Levenshtein distance, L, as a measure of similarity between normal and malicious SIP messages. We subject our dataset — consisting of malicious and normal SIP messages — to Euclidean distance-based classifiers as well as four standard classifiers. Our results show vast differences for Euclidean distance-based classifiers on our dataset than reported in current literature. We further see that the standard classifiers are better able to classify an anomalous message when L is small.

IEEE 802.15.4 specification for MAC and PHY offers a standard for general purpose wireless sensor networks. The TG4e of IEEE 802.15 is currently engaged in defining a specification particularly suitable for industrial and commercial applications, which impose severe constraints of low latency and high reliability. In this work, we present a simple MAC scheme to address these requirements of emergency response sensing applications for wireless sensor networks. We evaluate the proposed scheme for varying channel and traffic load conditions using simulations. Our results show that the latency and packet loss rate performance of emergency response traffic, consisting of guaranteed time slot (GTS) frames, significantly improves in comparison with the performance of original IEEE 802.15.4 MAC. The proposed MAC was also evaluated for potential adverse impact on the non-critical traffic, which consists of frames that use contention access period (CAP) of the superframe. It is shown that under realistic load conditions, the impact on the CAP traffic is minimal.

Industrial sensing, monitoring and automation offer a lucrative application domain for networking and communications. Wired sensor networks have traditionally been used for these applications because such networks adequately meet two vital requirements, i.e., low latency and high reliability, needed for an industrial deployment. Wired sensor networks, however, are not very cost effective due to higher components' cost. These networks also lack the flexibility needed for subsequent topological changes. Wireless sensor networks (WSN), on the other hand, are less expensive and offer high degree of flexibility. Wireless networks, therefore, can offer an attractive and viable solution for industrial sensing and automation. IEEE 802.15.4 standard defines a specification for MAC and PHY layers for short- range, low bit-rate, and low-cost wireless networks. However, the specified system is inefficient in terms of latency and reliability and fails to meet the stringent operational requirements for industrial applications. In this paper, we propose a set of new MAC superframes with an aim to enhance both performance metrics. We then use simulation to compare the performance of our proposed systems with that of the one specified in IEEE 802.15.4 standard.

Using a simulation study we analyze the throughput performance of Yao's adaptive ARQ scheme in time-varying channels. The simulation takes into account the Rayleigh amplitude and the fast or the slow fading characteristics of a wireless channel, under a representative M-FSK modulation and Reed-Solomon coding scheme. We show that, for a specific set of design parameters, Yao's adaptive procedure works well for all channel fading rates, except for moderately slow rates. By observing variations of packet error rates at a specified SNR we provide an explanation for these varied behaviors under different channel fading rates.

We describe a new medium access protocol termed the modified grouped-tag TDMA protocol (MGTDMA) for networking radio frequency identification tags. It is known that the previously proposed grouped-tag TDMA (GTDMA) protocol performs very well under the conditions of uniform destination distribution and not so well for heterogeneous traffic conditions. The MGTDMA differs from GTDMA in the sense that MCTDMA allows groups experiencing high traffic to steal (cooperatively) from low traffic groups at regular time intervals. The performance of an access scheme is assessed in terms of the average packet delay and average energy consumption. An approximate analytical equation for average delay is derived. More accurate estimates for delay are obtained through simulation studies. We compare the performance of MGTDMA, GTDMA, and a pseudo random protocol and show the usefulness of the new scheme.

We consider a general M-ary detection problem where, given M groups of L samples each, the problem is to identify which unique group of L samples have come from the signal hypothesis. The optimal likelihood ratio test is unrealizable, when the joint distribution of M L samples is not completely known. We consider two rank order types of tests termed the modified rank test (MRT) and the modified rank test with row sort (MRTRS). We examine through simulation, the small sample probability of error performances of MRT and MRTRS for detecting a signal among contaminants. Numerically comparable closed-form error expressions are derived for some special cases. The asymptotic (large sample) error rate of the MRT is also derived. The results indicate that MRTRS provides improved performance over other previously known rank tests.

This paper analyses a few schemes for combining base station antenna array signals in wireless DS/CDMA. The performances of equal gain combining (EGC), likelihood rank test (LRT) and a modified rank test (MRT) are evaluated using simulation studies. The results indicate that, under certain assumptions on the multiple access interference statistics, the probability of error of the MRT is lower than that of EGC, if a few high power interfering users are present along with a low power user of interest. If there are a moderately large number of users and if the received power of all the users are nearly the same, then EGC outperforms the MRT. In fact, under this condition, the performance of EGC is close to that of the optimal likelihood ratio test.

In this paper basic results on distributed detection are reviewed. In particular we consider the parallel and the serial architectures in some detail and discuss the decision rules obtained from their optimization based an the Neyman-Pearson (NP) criterion and the Bayes formulation. For conditionally independent sensor observations, the optimality of the likelihood ratio test (LRT) at the sensors is established. General comments on several important issues are made including the computational complexity of obtaining the optimal solutions the design of detection networks with more general topologies, and applications to different areas.

The usefulness of SNR as a figure of merit to quantify the narrowband interference rejection capability of a DS receiver is examined. The interference considered is a peaked autoregressive Gaussian process. The probability of error and SNR estimates of a Kalman, a modified Kalman, and a nonlinear filter proposed by Vijayan and Poor (see ibid., vol.38, no.7, p.1060, 1990) are obtained by simulation. Based on this simulation study and the available theoretical error rate analysis of transversal filters, we can conclude that SNR is a useful measure if the processing gain, PG, of the DS system is moderately large. When the PG is small, such as 7, and if thermal noise is negligible compared to the signal, the SNR is not a reliable measure of performance.>

We consider a decentralized detection problem where the prior density is not completely known, but is assumed to belong to an /spl epsiv/-contamination class. The expressions for the infimum and the supremum of the posterior probability that the parameter under question is in a given region, as the prior varies over the /spl epsiv/-contamination class, are derived. Numerical results are obtained for a specific case of an exponentially distributed observation and an exponentially distributed nominal prior. Asymptotic (as number of sensors tends to a large value) results are also obtained. The results illustrate the degree of robustness achieved with quantized observations as compared to unquantized observations.>

Linear least squares estimation techniques can be used to enhance suppression of narrowband interference in direct sequence spread spectrum systems. Nonlinear techniques for this purpose have also been investigated recently. The authors derive maximum likelihood receivers for direct sequence signal in Gaussian interference with known second order characteristics. It is shown that if the receiver uses samples from outside the bit interval, then the receiver structure is nonlinear. The bit error rate performances of these receivers are compared to those of linear receivers employing one-sided and two-sided least squares estimation filters, for the case of Gaussian autoregressive interference. The results show that intersymbol interference due to filter taps extending beyond the bit interval cannot be ignored for small processing gains. In some cases, not accounting for intersymbol interference yields too optimistic error estimates, very much away from the true error rates.>

The detection of a direct-sequence spread-spectrum signal received in a pulsed noise jamming environment is considered. The expectation-maximization algorithm is used to estimate the unknown jammer parameters and hence obtain a decision on the binary signal based on the estimated likelihood functions. The probability of error performance of the algorithm is simulated for a repeat code and a (7, 4) block code. Simulation results show that at low signal-to-thermal-noise ratio and high jammer power, the EM detector performs significantly better than the hard limiter and somewhat better than the soft limiter. Also, at low SNR, there is little degradation as compared to the maximum-likelihood detector with true jammer parameters. At high SNR, the soft limiter outperforms the EM detector.>

In relation to distributed parameter estimation, the notion of local and global sufficient statistics is introduced. It is shown that when a sufficiency condition is satisfied by the probability distribution of a random sample, a global sufficient statistic is obtainable as a function of local sufficient statistics. Several standard distributions satisfy the said sufficiency condition.>

A combiner for a frequency-hopped binary frequency-shift keyed (FH/BFSK) system, called the product-combining receiver (PCR), is introduced. The performance of the PCR is evaluated for the cases of an on/off partial-band noise with optimum-jamming fraction, and worst-case, partial-band tone jamming. The performance of PCR is shown to be comparable to that of the clipper receiver. The effect of diversity combining, along with convolutional coding and ratio-threshold technique, is analyzed. Whereas the clipper requires the knowledge of signal-to-noise ratio for threshold adjustments, the PCR does not require this knowledge for this operation.>

Various parametric receivers such as the maximum likelihood and the hard-limiter have been analyzed for their performance in decoding the frequency hopped multilevel FSK (FH-MFSK) messages in mobile environment. Here, some nonparametric receivers such as the maximum rank sum receiver (MRSR) and the reduced rank sum receiver (RRR) are considered. RRR and MRSR are nearly identical in performance but the former is much simpler to implement. The results indicate that RRR is a competing alternative to the parametric receivers.

The performances of three receivers, namely, the hardlimited, the linear, and the maximum-likelihood combiners for the detection of frequency-hopped multilevel frequency-shift keyed signals transmitted from mobiles to base have been reported earlier [11]. Only the hard-limited combiner has been analyzed with respect to base-to-mobile link [1]. Here, we give new results on the performance of the likelihood and the linear combining receivers operating at the mobiles. Whereas it is possible to find exactly the union bound on the probability of bit error for a linear combiner, for a likelihood receiver, bounding and approximation techniques such as simple Chernoff bound and saddle point integration were employed. We also observe the asymptotic (SNR \rightarrow \infty) equivalence of the hard-limited and the likelihood receivers. This, together with the approximate error estimates at finite SNR, leads us to believe that the likelihood receiver is only marginally superior to a hard-limited combiner. As expected, the linear combiner performs poorly.