Conventional battery management systems (BMSs) for electric vehicles (EVs) are designed in an ad hoc way, causing the supply of EVs to fall behind the market demand. A well-designed and combined hardware-software architecture is essential for the efficient management of a large-scale battery pack that may consists of thousands of battery cells as in Tesla Motors and GM Chevy Volt. We propose a Dependable, Efficient, Scalable Architecture (DESA) that effectively monitors a large number of battery cells, efficiently controls, and reconfigures, if needed, their connection arrangement. DESA supports hierarchical, autonomous management of battery cells, where a global BMS orchestrates a group of local BMSs. A local controller on each local BMS autonomously manages an array of battery cells, and the global controller reconfigures the connectivity of such battery-cell arrays in coordination with the local controllers. Also, DESA allows individual arrays and local BMSs to be selectively powered-off for energy savings. The performance of this energy-saving capability is modeled and evaluated using a Markov chain. Our evaluation results show that DESA effectively tolerates battery-cell failures by an order-of-magnitude—while achieving$7.4\times$service cost savings—better than a conventional BMS. This superior performance not only extends the battery life significantly, but also provides the flexibility in supporting diverse electric power demands from a growing number of on-board applications.

It is of great importance to protect rapidly-spreading and widely-used small mobile devices like smartphones and PocketPCs from energy-depletion attacks by monitoring software (processes) and hardware (especially, battery) resources. The ability to use these devices for on- and/or off-job functions, and even for medical emergencies or disaster recovery is often dictated by their limited battery capacity. However, traditional malware detection systems and antivirus solutions based on matching signatures are limited to detection of only known malware, and hence, cannot deal with battery-depletion attacks. To meet this challenge, we propose to develop, implement, and evaluate a comprehensive framework, called MODELZ, that MOnitors, DEtects, and anaLyZes energy-greedy anomalies on small mobile devices. MODELZ comprises 1) a charge flow meter that allows infrequent sampling of energy consumption without losing accuracy, 2) a power monitor, in coordination with the charge flow meter, that samples and builds a power-consumption history, and 3) a data analyzer that generates a power signature from the power-consumption history. To generate a power signature, we devise and apply light-weighted, effective noise filtering and data compression, reducing the detection overhead significantly. The similarities between power signatures are measured by the χ2-distance and used to lower both false-positive and false-negative detection rates. Our experimental results on an HP iPAQ running the Windows Mobile OS have shown that MODELZ achieves significant (up to 95 percent) storage-savings without losing detection accuracy, and a 99 percent true-positive rate in differentiating legitimate programs from suspicious ones while the monitoring consumes 50 percent less energy than the case of keeping the Bluetooth radio turned on.

Abstract While mobile nodes (MNs) undergo handovers across inter‐wireless access networks, their security contexts must be propagated for secure re‐establishment of on‐going application sessions, such as those in secure mobile internet protocol (IP), authentication, authorization, and accounting (AAA) services. Routing security contextsviaan IP network either on‐demand or based on MNs' mobility prediction, imposes new challenging requirements of securecross‐handoverservices and security context management. In this paper, we present acontext router(CXR) that manages security contexts in an all‐IP network, providing seamless and secure handover services for the mobile users that carry multimedia‐access devices. A CXR is responsible for (1) monitoring of MNs' cross‐handover, (2) analysis of MNs' movement patterns, and (3) routing of security contexts ahead of MNs' arrival at relevant access points. The predictive routing reduces the delay in the underlying security association that would otherwise fetch an involved security context from a remote server. The predictive routing of security contexts is performed based on statistical learning of MNs' movement pattern, gauging (dis)similarities between the patterns obtainedviadistance measurements. The CXR has been evaluated with a prototypical implementation based on an MN mobility model on a grid. Our evaluation results support the predictive routing mechanism's improvement in seamless and secure cross‐handover services by a factor of 2.5. Also, the prediction mechanism is shown to outperform the Kalman filter‐based method [13] as a Kalman Fiter‐based mechanism up to 1.5 and 3.6 times regarding prediction accuracy and computation performance, respectively. Copyright © 2009 John Wiley & Sons, Ltd.

While mobile nodes (MNs) undergo handovers across inter-wireless access networks, their contexts must be propagated for seamless re-establishment of on-going application sessions, including IP header compression, secure Mobile IP, authentication, authorization, and accounting services, to name a few. Routing contexts via an overlay network either on-demand or based on prediction of an MNs' mobility, introduces a new challenging requirement of context management. This paper proposes a context router (CXR) that manages contexts in an overlay network. A CXR is responsible for (1) monitoring of MNs' cross-handover, (2) analysis of MNs' movement patterns, and (3) context routing ahead of each MN's arrival at an AP or a network. The predictive routing of contexts is performed based on statistical learning of (dis)similarities between the patterns obtained from vector distance measurements. The proposed CXR has been evaluated on a prototypical implementation based on an MN mobility model in an emulated access network. Our evaluation results show that the prediction mechanisms applied on the CXR outperform a Kalman-filter-based method [34] with respect to both prediction accuracy and computation performance.

Electric vehicles powered with large-scale battery packs are gaining popularity as gasoline price soars. Large-scale battery packs usually consist of an estimated 12,000 battery cells connected in series and parallel, which are susceptible to battery-cell failures. Unfortunately, current battery-management systems are unable to handle the inevitable battery-cell failures very well. To address this problem, we propose a dynamic reconfiguration framework that monitors, reconfigures, and controls large-scale battery packs online. The framework is built upon a syntactic bypassing mechanism that provides a set of rules for changing the battery-pack configuration, and a semantic bypassing mechanism by which the battery-cell connectivity is reconfigured to recover from a battery-cell failure. In particular, the semantic bypassing mechanism is dictated by constant-voltage-keeping and dynamic-voltage-allowing policies. The former policy is effective in preventing unavoidable voltage drops during the battery discharge, while the latter policy is effective in supplying different amounts of power to meet a wide-range of application requirements. Our experimental evaluation has shown the proposed framework to enable the battery packs to be 9 times as fault-tolerant as a legacy scheme.

Electric vehicles operate inefficiently with a naive battery management system that charges or discharges battery cells in a pack based solely on application load demands. The battery pack's operation-time and lifetime can be extended significantly by effectively scheduling (the cyber part) battery charge, discharge, and rest activities, based on the battery characteristics (the physical part). We propose a set of policies for scheduling battery-cell activities, called the weighted-k round-robin (kRR) scheduling framework. This framework dynamically adapts battery-cell activities to load demands and the condition of individual cells, thereby extending the battery pack's operation-time and making them robust to anomalous voltage-imbalances. The framework comprises two key components. First, an adaptive filter estimates the upcoming load demand. Then, based on the estimated load demand, the kRR scheduler determines the number of parallel-connected cells to be discharged simultaneously. The scheduler also effectively partitions the cells in the pack, allowing the cells to be simultaneously charged and discharged in coordination with the battery reconfiguration system we developed earlier [17]. Besides the kRR scheduling framework, we characterize the discharge and recovery efficiency of a Lithium-ion battery cell. The kRR scheduling framework is shown to outperform three alternative scheduling mechanisms with respect to the operation-time by 7-56%, and improve the tolerance of voltage-imbalance by up to 50%.

Mobile users of computation and communication services have been rapidly adopting battery-powered mobile handhelds, such as PocketPCs and SmartPhones, for their work. However, the limited battery-lifetime of these devices restricts their portability and applicability, and this weakness can be exacerbated by mobile malware targeting depletion of battery energy. Such malware are usually difficult to detect and prevent, and frequent outbreaks of new malware variants also reduce the effectiveness of commonly-seen signature-based detection. To alleviate these problems, we propose a power-aware malware-detection framework that monitors, detects, and analyzes previously unknown energy-depletion threats. The framework is composed of (1) a power monitor which collects power samples and builds a power consumption history from the collected samples, and (2) a data analyzer which generates a power signature from the constructed history. To generate a power signature, simple and effective noise-filtering and data-compression are applied, thus reducing the detection overhead. Similarities between power signatures are measured by the χ2-distance, reducing both false-positive and false-negative detection rates. According to our experimental results on an HP iPAQ running a Windows Mobile OS, the proposed framework achieves significant (up to 95%) storage-savings without losing the detection accuracy, and a 99% true-positive rate in classifying mobile malware.

The software approach to developing Digital Signal Processing (DSP) applications brings some great features such as flexibility, re-usability of resources and easy upgrading of applications. However, it requires long and tedious tests and verification phases because of the increasing complexity of the software applications. This implies the need of a software programming environment capable of putting together DSP modules and providing facilities to debug, verify and validate the code. The objective of the work is to provide such facilities as simulation and verification for developing DSP software applications. This led us to developing an extension toolkit, EPSPECTRA, built upon PSPECTRA, one of the first toolkits available to design basic software radio applications on standard PC workstations. In this paper, we first present EPSPECTRA, an ESTEREL-based extension of PSPECTRA that makes the design and implementation of portable DSP applications easier. It allows the drastic reduction of testing and verification time while requiring relatively little expertise in formal verification methods. Second, we demonstrate the use of EPSPECTRA, taking as an example the radio interface part of a GSM base station. We also present the verification procedures for the three safety properties of the implementation programs which have complex control-paths. These have to obey strict scheduling rules. In addition, EPSPECTRA achieves the verification of the targeted application since the same model is used for the executable code generation and for the formal verification.

As mobile users cross the border of two adjacent domains with on-going sessions, their re-authentication causes a significant impact on inter-domain handoff latency as it requires remote contact with the authentication server across domains, making it difficult to employ current authentication protocols. This paper focuses on the cross-domain authentication over wireless local area networks (WLANs) that minimizes the need for remote access. We analyze the security requirements suggested by the IEEE 802.11i authentication standard, and consider additional requirements to help reduce the authentication latency without compromising the level of security. We propose an enhanced protocol called the Mobility-adjusted Authentication Protocol (MAP) that performs mutual authentication and hierarchical key derivation with minimal handshakes, relying on symmetric cryptographic functions. We also present security context nodes (SCNs) that handle security contexts in conjunction with MAP, which allows for avoiding continuous remote contact with the home authentication server. In contrast to Kerberos which favors inter-realm authentication, MAP achieves a 26% reduction of authentication latency without degrading the level of security.

This paper presents a security scheme that aims at automating the PIN exchange procedure with the help of the AAA infrastructure in WiFi-overlapped WPAN (wireless personal access network). The proposed security scheme contributes to providing an automatic set-up for the security channel, particularly between a pair of unknown Bluetooth devices.

The rapid growth of wireless technology and the increasing use of such technologies in coordination with the Internet demand a very careful look at issues related to security. As more and more users attempt to utilize such technologies in the context of providing security demanding services, it is essential to recognize the potential threats in wireless technologies. This paper focuses on authentication as part of the significant issues related to security and proposes a new authentication method combining the AAA framework and the UMTS security. As for that, this work presents results that show how the concepts of AAA and the USIM mechanism can be combined for the adaptability of mobile environment as well as for the efficient authentication. It also presents the specification and the implementation of the combination of diameter of the AAA protocols and USIM. In addition to the design implementation, this work reports on the performance results showing that this combination can be a good solution to authentication in a mobile environment.