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The logical separation of control signaling from data transmission in a mobile cellular network has been shown to have significant energy saving potential compared with the legacy systems. As a result, there has been a lot of focus in recent years on development and realization of separation architectures. Our study, however, shows that the energy savings of separation architecture remain under 16-17% when compared with legacy systems and this gain falls to a mere 7% when both architectures are realized under a CloudRAN (CRAN) setting. Moreover, when we strategically place some small base-stations (SBSs) to cover the area in a densely deployed scenario and allow all other base-stations (BSs) to be used only on-demand, the system consumes much less energy than the separation architecture. While we expected that most equipment would be shut down during nights, our study shows that around 70% of the small cells are required to be active to serve randomly distributed minimum data load, i.e., active mobile equipment. Contemporary mobile traffic is predominantly data which does not go to extremely low levels during nights. We discuss, in detail, the assumptions, their implications, and the effects of system parameter values on our conclusions.

We propose an integrated scheme for tracking the mobility of a user based on autoregressive models that accurately capture the characteristics of realistic user movements in wireless networks. The mobility parameters are obtained from training data by computing Minimum Mean Squared Error (MMSE) estimates. Estimation of the mobility state, which incorporates the position, velocity, and acceleration of the mobile station, is accomplished via an extended Kalman filter using signal measurements from the wireless network. By combining mobility parameter and state estimation in an integrated framework, we obtain an efficient and accurate real-time mobility tracking scheme that can be applied in a variety of wireless networking applications. We consider two variants of an autoregressive mobility model in our study and validate the proposed mobility tracking scheme using mobile trajectories collected from drive test data. Our simulation results validate the accuracy of the proposed tracking scheme even when only a small number of data samples is available for initial training.

Anomaly detection is becoming a powerful and necessary component as wireless networks gain popularity. In this paper, we evaluate the efficacy of PCA based anomaly detection for wireless mesh networks (WMN). PCA based method [1] was originally developed for wired networks. Our experiments show that it is possible to detect different types of anomalies, such as Denial-of-service (DoS) attack, port scan attack [1], etc., in an interference prone wireless environment. However, the PCA based method is found to be very sensitive to small changes in flows causing non-negligible number of false alarms. This problem prompted us to develop an anomaly identification scheme which automatically identifies the flow(s) causing the detected anomaly and their contributions in terms of number of packets. Our results show that the identification scheme is able to differentiate false alarms from real anomalies and pinpoint the culprit(s) in case of a real fault or threat. Moreover, we also found that the threshold value used in [1] for distinguishing normal and abnormal traffic conditions is based on assumption of normally distributed traffic which is not accurate for current network traffic which is mostly self-similar in nature. Adjusting the threshold also reduced the number of false alarms considerably. The experiments were performed over an 8 node mesh testbed deployed in a suburban area, under different realistic traffic scenarios. Our identification scheme facilitates the use of PCA based method for real-time anomaly detection in wireless networks as it can filter the false alarms locally at the monitoring nodes without excessive computational overhead.

Anomaly detection is emerging as a necessary component as wireless networks gain popularity. Anomaly detection has been addressed broadly in wired networks and powerful methods have been developed for correct detection of a variety of known attacks and other anomalies. In this paper, we propose a real-time anomaly detection and identification scheme for wireless mesh networks (WMN) using components from previous methods developed for wired networks. Experiments over a WMN testbed show the effectiveness of the proposed scheme in isolating different types of anomalies, such as Denial-of-service attacks, port scan attacks, etc. Our scheme uses Chi-square statistics and it is based on similar ideas as the scheme presented by Lakhina et al. although it has lower computational complexity. The original method by Lakhina et al. was developed for wired networks and used Principal Component Analysis (PCA) for reducing the dimensions of observed data and Hotelling's t 2 statistics to distinguish between normal and abnormal traffic conditions. However, in our studies we found that dimension reduction is the most computationally intensive process of the scheme. In this paper we propose an alternative way of reducing dimensions using flow variances in a Chi-square test. Experimental results show that the Chi-square test performs similarly well to the PCA-based method at merely a fraction of the computations. Moreover, we propose an automatic identification scheme to pin-point the cause of the detected anomaly and its contribution in terms of additional or lack of traffic. Our results and comparison with other statistical tools show that the Chi-square test and the PCA-based method with identification scheme make powerful tools for real-time detection of various anomalies in an interference prone wireless networking environment.

In this paper, we present a monitoring assisted routing scheme for wireless mesh networks which exploits the broadcast nature of wireless transmissions at special routers with added monitoring functionalities. These routers listen to the transmissions in neighborhood and compare the routing behavior against the routing state collectively maintained by them. If any inconsistency is found, as a result of software/hardware malfunction, these routers try to determine the node causing it and recover from it in a timely fashion. The scheme is developed for wireless mesh networks where the communication overhead is a critical issue. The performance evaluation of our scheme shows considerable improvement in reliability (i.e., delivery ratio) with minimal overhead under situations of malfunctions.

Wireless mesh networks (WMN) have attracted considerable interest in recent years as a convenient, flexible and low-cost alternative to wired communication infrastructures in many contexts. However, the great majority of research on metropolitan-scale WMN has been centered around maximization of available bandwidth, suitable for non-real-time applications such as Internet access for the general public. On the other hand, the suitability of WMN for mission-critical infrastructure applications remains by and large unknown, as protocols typically employed in WMN are, for the most part, not designed for real-time communications. In this paper, we describe the Smart Transport and Roads Communications (STaRComm) project at National ICT Australia (NICTA), which sets a goal of designing a wireless mesh network architecture to solve the communication needs of the traffic control system in Sydney, Australia. This system, known as SCATS (Sydney Coordinated Adaptive Traffic System)and used in over 100 cities around the world, connects a hierarchy of several thousand devices -- from individual traffic light controllers to regional computers and the central Traffic Management Centre (TMC)-- and places stringent requirements on the reliability and latency of the data exchanges. We discuss our experience in the deployment of an initial testbed consisting of 7 mesh nodes placed at intersections with traffic lights, and share the results and insights learned from our measurements and initial trials in the process.

We propose two algorithms for real-time tracking of the location and dynamic motion of a mobile station in a cellular network using the pilot signal strengths from neighboring base stations. The underlying mobility model is based on a dynamic linear system driven by a discrete command process that determines the mobile station's acceleration. The command process is modeled as a semi-Markov process over a finite set of acceleration levels. The first algorithm consists of an averaging filter for processing pilot signal, strength measurements and two Kalman filters, one to estimate the discrete command process and the other to estimate the mobility state. The second algorithm employs a single Kalman filter without prefiltering and is able to track a mobile station even when a limited set of pilot signal measurements is available. Both of the proposed tracking algorithms can be used to predict future mobility behavior, which can be, useful in resource allocation applications. Our numerical results show that the proposed tracking algorithms perform accurately over a wide range of mobility parameter values.

Rural areas have very low revenue potential. The major issue in providing low-cost broadband to rural areas is to provide reliable backhaul connections that spread over tens or even hundreds of miles, connecting villages to the nearest service provider. Along with aerial networks of Google and Facebook, there has been a considerable amount of research toward long-distance terrestrial WiFi links. As a comparison, WiFi routers are easier to be deployed and maintained by non-technical people from the local communities, whereas the aerial networks require professional support to operate. Moreover, they are still in the experimentation phase. However, the long distance WiFi links require high-gain directional antennas and very expensive tall towers for high data rates. On the other hand, multihop paths with stronger links may provide better data rates without the need of tall towers. In this paper, we evaluated the concept of using such multihop WiFi links for long backhaul connections. Our simulation results show that these networks can possibly be a cost-effective and practical solution for rural connectivity. These initial results can serve as a first step to understand the comprehensive feasibility of using multihop WiFi networks for backhaul connections in rural area.

In this position paper, we identify the potential of an integrated deployment solution for energy efficient cellular networks combining the strengths of two very active research themes: software defined radio access networks (SD-RAN) [1] and decoupled signaling and data transmissions, or beyond cellular green generation (BCG2) architecture, for enhanced energy efficiency [2]. While SD-RAN envisions a decoupled centralized control plane and data forwarding plane for flexible control, the BCG2 architecture calls for decoupling coverage from capacity and coverage is provided through always-on low-power signaling node for a larger geographical area; capacity is catered by various on-demand data nodes for maximum energy efficiency. In this paper, we identify that a combined approach bringing in both specifications together can, not only achieve greater benefits, but also facilitates the faster realization of both technologies. We propose the idea and design of a signaling controller which acts as a signaling node to provide always-on coverage, consuming low power, and at the same time also hosts the control plane functions for the SD-RAN through a general purpose processing platform. Phantom cell concept is also a similar idea where a normal macro cell provides interference control to densely deployed small cells [3], although, our initial results show that the integrated architecture has much greater potential of energy savings in comparison to phantom cells.

In this paper, reliability of WiFi multihop backhaul connections, in terms of probability of successful packet delivery, is analyzed in detail. The major issue in providing a cost-effective rural broadband network is to connect the remote sites to the existing network and with each other over distances of tens or hundreds of miles. There has been a lot of research activity around long distance WiFi links. These links require high-gain directional antennas and very expensive tall towers to maintain LoS (Line-of-Sight). In this paper, we evaluated, using empirical models, the idea of using multiple hop connections in linear topology, also termed as multihop chains, for rural backhaul. Reliability of WiFi chains is evaluated in adverse conditions of radio propagation and in the presence of node failures. This analysis shows promising potential of cost-effective WiFi chain networks as backhauls with reasonable reliability. Considerable improvement in reliability and robustness can be achieved by providing some redundancy in network topology. Networks with redundant nodes can tolerate larger separation between adjacent nodes, thus reducing the overall deployment cost.

ETX (Expected Transmission Count) is shown to be the best link metric in terms of higher throughput for wireless multi-hop network with stationary-nodes having single radio. Some recent work, however, shows that ETX may exhibit high sensitivity to interference and a single TCP flow can increase the metric value by 10000% [1]. Such a change indicates that routing protocols, based on ETX, might be playing with random numbers without bringing any significant benefits of considering link quality. It is suggested that broadcast-based estimation of ETX could be the major cause of sensitivity as other metrics based on unicast probing method did not show similar performance degradation [1]. Several proposals came forward so far focusing on better estimation techniques of ETX metric mostly using unicast probing combined with cross-layer information; however, we have not seen any investigation study as to why does broadcast-based method result in inaccurate estimation of ETX. This paper presents preliminary results of an investigation study about the sensitivity of ETX estimation based on broadcast probing and compare it with the one based on unicast probing. It is work-in-progress.

HELLO messages are widely used for neighbor discovery in routing protocols for wireless multihop networks. In this paper, we provide mathematical and experimental proofs that the current strategy of declaring a link down based on observations that a specific number of HELLO messages are lost, is not a correct strategy. In our mathematical analysis, we characterized the error bursts over wireless links by extending the Gilbert bit error model, and validate our model using HELLO loss data from our wireless mesh testbed. Our analysis shows that error burst lengths follow a geometric distribution, i.e. the probability of additional losses in the burst does not depend on the observed losses. We propose an alternative link break detection strategy where a link break is declared when the observed mean error burst length for the link is longer than the mean route recovery time. Our testbed results show that running the AODV routing protocol with our proposed strategy yields better throughput and lower control message overhead, compared to the case where AODV relies on its default settings of declaring link breaks when two consecutive HELLO messages are lost.

ETX (Expected Transmission Count) is the best routing metric for single radio wireless mesh network (WMN) in terms of throughput as it selects the links with highest packet delivery paths. In our experiments, however, we found that under some scenarios which commonly occur in normal practice, e.g., when data rates approach channel capacity, ETX could yield lower throughput than other metrics, such as, minimum hop-count or RTT (Round Trip Time). The paper presents the experimental evidence of performance anomaly in ETX, identifies the operating conditions when anomaly occurs, and discusses the results of our investigation regarding potential causes, such as, probing overhead, load-sensitivity, etc., using simulations and testbed experiments. Results show that probing, though incurs small overhead, plays a major role in performance degradation of ETX. When node buffers are full because of higher data rate, prioritized control packets, including probes, push data packets outside the queue. When alternative paths are not very much different to each other, ETX appears worse than hop-count because of this additional buffer overflow loss. Although, this loss becomes insignificant when penalty of selecting a lossy path is higher. We also observed that ETX exhibits route oscillations due to load-sensitivity but strong correlation of its load-sensitivity with degraded performance is not found.

We propose an integrated scheme for estimating the mobility state and model parameters of a user based on a first-order autoregressive model of mobility that accurately captures the characteristics of realistic user movements in wireless networks. Estimation of the mobility parameters is performed by applying the Yule-Walker equations to the training data. Estimation of the mobility state, which consists of the position, velocity, and acceleration of the mobile station is accomplished via an extended Kalman filter using measurements from the wireless network. The integration of mobility state and model parameter estimation results in an efficient and accurate real-time mobility tracking scheme that can be applied in a variety of wireless networking applications. The mobility estimation scheme can also be used to generate realistic mobility patterns to drive computer simulations of mobile networks. We validate the proposed mobility estimation scheme using mobile trajectories collected from drive-test data obtained from a live cellular network.

We propose a novel scheme for tracking the mobility of users in a wireless ad hoc network. Mobile nodes track their positions using pilot signal strengths from neighboring nodes within a local coordinate system based on relative distances between nodes. Node mobility is modeled as a linear system driven by a discrete command semi-Markov process. Mobility tracking is performed using an extended Kalman filter preceded by an averaging filter. Our numerical results show that the mobility tracking scheme performs effectively and can be used to enhance routing performance in ad hoc networks.

We propose a robust estimation algorithm for tracking the location and dynamic motion of a mobile unit in a cellular network. The underlying mobility model is a dynamic linear system driven by a discrete command process that determines the mobile unit's acceleration. The command process is modeled as a semi-Markov process over a finite set of acceleration levels. Our proposed tracking algorithm is based on a modified Kalman filter in combination with an efficient hidden semi-Markov model (HSMM) estimation algorithm to estimate the parameters of the command process. Numerical results show that the proposed tracking algorithm performs accurately over a wide range of mobility parameter values.