Many electricity suppliers around the world are deploying smart meters to gather fine-grained spatiotemporal consumption data and to effectively manage the collective demand of their consumer base. In this paper, we introduce a structured framework and a discriminative index that can be used to segment the consumption data along multiple contextual dimensions such as locations, communities, seasons, weather patterns, holidays, etc. The generated segments can enable various higher-level applications such as usage-specific tariff structures, theft detection, consumer-specific demand response programs, etc. Our framework is also able to track consumers’ behavioral changes, evaluate different temporal aggregations, and identify main characteristics which define a cluster.

WiFi-based indoor localization solutions are actively in commercial use today. WiFi radio maps are typically created in an offline process, and location estimation is performed in realtime (online) using the maps. Both the radio map and the signal strengths provided as input during the online phase are affected by several dynamic factors resident in the environment. We call these “causality factors”. Hence, the online location accuracy is far from the quality achieved in laboratory tests with training data, since the causality factors have varied in between. This impacts the quality expected by heterogeneous applications in real deployments. To address this issue, we investigate a novel online dynamic calibration methodology called KARMA. KARMA utilizes a one-time fingerprint of the space and systematically applies a set of causality calibration functions in real-time to compensate for the change in the factors, at test time. As a result, location providers can now significantly reduce the costly re-learning of the models for different factors, and improve real-time location prediction accuracy. Experimental studies demonstrate that KARMA's strategy, while keeping the fingerprinting task contained, can improve localization quality by a factor of 2x, compared to a typical one-state fingerprinting approach employed in many commercial deployments today. It also compares very favorably with an exhaustive all-state fingerprinting approach.

The continuous growth of energy needs and the fact that unpredictable energy demand is mostly served by unsustainable (i.e. fossil-fuel) power generators have given rise to the development of Demand Response (DR) mechanisms for flattening energy demand. Building effective DR mechanisms and user awareness on power consumption can significantly benefit from fine-grained monitoring of user consumption at the appliance level. However, installing and maintaining such a monitoring infrastructure in residential settings can be quite expensive. In this paper, we study the problem of fine-grained appliance power-consumption monitoring based on one house-level meter and few plug-level meters. We explore the trade-off between monitoring accuracy and cost, and exhaustively find the minimum subset of plug-level meters that maximize accuracy. As exhaustive search is time- and resource-consuming, we define a heuristic approach that finds the optimal set of plug-level meters without utilizing any other sets of plug-level meters. Based on experiments with real data, we found that few plug-level meters-when appropriately placed — can very accurately disaggregate the total real power consumption of a residential setting and verified the effectiveness of our heuristic approach.

There is tremendous interest in exploiting smartphones as a “community of sensors”. It is envisioned that this community-driven smartphone sensor network has unprecedented potential to sense heterogeneous phenomena ranging from sound pollution to urban social dynamics. However, designing smartphone-resident middleware for opportunistic and objectiveoriented sensing of these phenomena is an open challenge. In this paper, we propose USense, a novel utility-driven smartphone middleware for executing community-driven sensing tasks. USense is different from other mobile phone sensing frameworks in the following ways: it is (i) Application aware, i.e., it adapts its operation based on demands of the application (ii) User aware, i.e., it incorporates preferences, policies as well as behavioral history of the user carrying the phone, and (iii) Situation aware, i.e. it considers resource dynamics on the phone at any given point. We argue that these three aspects are essentially decoupled in nature and combining them effectively is the key towards designing a re-usable and scalable middleware. Based on an extensible model for `Sensing Moments', USense first allows application developers to easily create sensing tasks. Secondly, we propose a unified device middleware to simultaneously execute the sensing tasks at the right moments across multiple applications. We have implemented USense on the Android platform, and demonstrate its effectiveness through real-life data traces.

Efficiently querying data collected from Large-area Community driven Sensor Networks (LCSNs) is a new and challenging problem. In our previous works, we proposed adaptive techniques for learning models (e.g., statistical, nonparametric, etc.) from such data, considering the fact that LCSN data is typically geo-temporally skewed. In this paper, we present a demonstration of EnviroMeter. EnviroMeter uses our adaptive model creation techniques for processing continuous queries on community-sensed environmental pollution data. Subsequently, it efficiently pushes current pollution updates to GPS-enabled smartphones (through its Android application) or displays it via a web-interface. We experimentally demonstrate that our model-based query processing approach is orders of magnitude efficient than processing the queries over indexed raw data.

With the large-scale adoption of GPS equipped mobile sensing devices, positional data generated by moving objects (e.g., vehicles, people, animals) are being easily collected. Such data are typically modeled as streams of spatio-temporal (x,y,t) points, called trajectories . In recent years trajectory management research has progressed significantly towards efficient storage and indexing techniques, as well as suitable knowledge discovery. These works focused on the geometric aspect of the raw mobility data. We are now witnessing a growing demand in several application sectors (e.g., from shipment tracking to geo-social networks) on understanding the semantic behavior of moving objects. Semantic behavior refers to the use of semantic abstractions of the raw mobility data, including not only geometric patterns but also knowledge extracted jointly from the mobility data and the underlying geographic and application domains information. The core contribution of this article lies in a semantic model and a computation and annotation platform for developing a semantic approach that progressively transforms the raw mobility data into semantic trajectories enriched with segmentations and annotations. We also analyze a number of experiments we did with semantic trajectories in different domains.

In pervasive and ubiquitous computing systems, human activity recognition has immense potential in a large number of application domains. Current activity recognition techniques (i) do not handle variations in sequence, concurrency and interleaving of complex activities; (ii) do not incorporate context; and (iii) require large amounts of training data. There is a lack of a unifying theoretical framework which exploits both domain knowledge and data-driven observations to infer complex activities. In this article, we propose, develop and validate a novel Context-Driven Activity Theory (CDAT) for recognizing complex activities. We develop a mechanism using probabilistic and Markov chain analysis to discover complex activity signatures and generate complex activity definitions. We also develop a Complex Activity Recognition (CAR) algorithm. It achieves an overall accuracy of 95.73% using extensive experimentation with real-life test data. CDAT utilizes context and links complex activities to situations, which reduces inference time by 32.5% and also reduces training data by 66%.

Effectively managing the data generated by Large-area Community driven Sensor Networks (LCSNs) is a new and challenging problem. One important step for managing and querying such sensor network data is to create abstractions of the data in the form of models. These models can then be stored, retrieved, and queried as required. In our OpenSense project, we advocate an adaptive model-cover driven strategy towards effectively managing such data. Our strategy is designed considering the fundamental principles of LCSNs.

One of the biggest challenges in the field of activity recognition is gathering training data for building activity inference models. To address this problem, we have developed an online activity toolkit for gathering activity data from online users. We use this data to build activity definitions for use in our system which is based on Context-Driven Activity Theory. We use Markov chain analysis to assign weights to activities and context attributes of a complex activity as well as to build activity signatures based on transition and path probabilities. Our demo is intended to show how complex activities and associated atomic activities and context attributes can be described using an activity toolkit. The toolkit is used to take input from users available online and the results analysis of different complex activities can be viewed online in near real-time using the graphical user interface (GUI).

Effectively managing the data generated by community-driven mobile geo-sensor networks is a new and challenging problem. One important step for managing and querying sensor network data is to create abstractions of the data in the form of models. These models can then be stored, retrieved, and queried, as required. There has been significant amount of prior literature on using models for query processing [1]-[5]. On the contrary, however, there has been a lack of understanding on developing reliable models, considering the unique characteristics of community-driven geo-sensor networks. In an effort to correct this situation, this paper proposes various approaches for modeling the data from a community-driven mobile geo-sensor network. This data is typically collected over a large geographical area with mobile sensors having uncontrolled or semi-controlled mobility. Therefore, we propose adaptive techniques that take into account such mobility patterns and produce an accurate representation of the sensed spatiotemporal phenomenon. To substantiate our proposals, we perform extensive evaluation of our methods on two real datasets.

Social networks constitute an important research area where users are involved in social interactions and inform each other of activities they perform. In this paper a complex activity recognition system (SEMACT) is proposed, built and validated. Different activities performed by users form an activity hierarchy. We perform semantic reasoning by using ontological constructs and rules to recognize both concurrent and interleaved complex activities at different levels of granularity of this activity hierarchy. Different application domains require activity recognition systems to define and recognize activities at different levels of granularity. Our system tackles this problem by recognizing complex activities which are then shared across application domains using a generic API. A test-bed and prototype are built to validate our SEMACT system. Extensive experimentation is performed which demonstrates that high accuracy of 94.35% was achieved for the recognition of complex activities both concurrent and interleaved within computationally feasible time.

GPS devices allow recording the movement track of the moving object they are attached to. This data typically consists of a stream of spatio-temporal (x,y,t) points. For application purposes the stream is transformed into finite subsequences called trajectories. Existing knowledge extraction algorithms defined for trajectories mainly assume a specific context (e.g. vehicle movements) or analyze specific parts of a trajectory (e.g. stops), in association with data from chosen geographic sources (e.g. points-of-interest, road networks). We investigate a more comprehensive semantic annotation framework that allows enriching trajectories with any kind of semantic data provided by multiple 3rd party sources.

This paper proposes a situation and context-aware complex activity recognition system where we use on-body sensor information along with other context information to infer mobile user activities which are both concurrent and interleaved. We develop and validate our complex activity recognition algorithm for detecting these multiple complex activities. Activities are mapped onto situations using spatio-temporal analysis. We further build a test-bed in the social-networking domain to test and validate our approach in different scenarios using mobile devices.

With the prevalence of GPS-embedded mobile devices, enormous amounts of mobility data are being collected in the form of trajectory - a stream of (x,y,t) points. Such trajectories are of heterogeneous entities - vehicles, people, animals, parcels etc. Most applications primarily analyze raw trajectory data and extract geometric patterns. Real-life applications however, need a far more comprehensive, semantic representation of trajectories. This paper demonstrates the automatic construction and visualization capabilities of SeMiTri - a system we built that exploits 3rd party information sources containing geographic information, to semantically enrich trajectories. The construction stack encapsulates several spatio-temporal data integration and mining techniques to automatically compute and annotate all meaningful parts of heterogeneous trajectories. The visualization interface exhibits different levels of data abstraction, from low-level raw trajectories (i.e. the initial GPS trace) to high-level semantic trajectories (i.e. the sequence of interesting places where moving objects have passed and/or stayed).

Recent technological advances in mobile-based access to social networking platforms and facilities to update information in real{time (e.g. in Facebook) have allowed an individual's online presence to be as ephemeral and dynamic in nature, as her very thoughts and interests. In this context, micro-blogging has been widely adopted by users as an effective means to capture and disseminate their thoughts and actions to a larger audience on a daily basis. Interestingly, daily chatters of a user obtained from her micro-blogs offer a unique information source to analyze and interpret her context in real-time - i.e. interests, intentions,and activities. In this paper, we gather data from the public timeline of Twitter spanning across ten worldwide cities over a period of four weeks. We use this dataset to (a) explore how users express interests in real-time through micro-blogs, and (b) understand how text mining techniques can be applied to interpret real-time context of a user based on her tweets. Initial findings reported herein suggest that social media sites like Twitter constitute a promising source for extracting user context that can be exploited by novel social networking applications.

Many services taken for granted in the developed world are often missing from the developing countries. One typical example is that of map systems that form the basis of several location driven services. Its heavy reliance on content, provides a huge barrier towards building such systems. Further, in developing countries like India, the infrastructure typically has a history of unplanned development, leading to unorganized addresses and absence of standard naming conventions for roads. Detailed map systems such as online maps have only recently started becoming available but for major cities. Remote towns and villages remain out of reach till date. In this paper, we investigate a community-driven approach for creating maps in developing regions - following Web 2.0 principles, but not entirely relying on the existing Web. Our system, dubbed FOLKSOMAPS is an intelligent, community constructed map system, particularly architected with developing regions in mind. We present the design of FOLKSOMAPS, followed by an implementation of our proof-of-concept system. We present user studies aimed at understanding the uptake, usability and utility of FOLKSOMAPS. The results indicate a strong need for such a community-generated mapping ecosystem.

With the growing popularity of social networking, traditional Internet Service Providers (ISPs) and telecom operators have both started exploring new opportunities to boost their revenue streams. The efforts have facilitated consumers to stay connected to their favorite social networks,be it from an ISP portal or a mobile device. The use of Web 2.0 technologies and converged communication tools has further led to a rise in both user-generated content as well as contextual information (i.e. rich presence) about users - including their current location, availability, interests and moods. In this evolving landscape, social networking players need to innovate for value-centric usage models that increase customer stickiness,along with business models to monetize the social media. To this end, we present R-U-In? - an activity-oriented social networking system for users to collaborate and participate in activities of mutual interest. Activities can be initiated and scheduled on-demand and be as ephemeral as the user interests themselves. R-U-In? leverages contextual modeling and reasoning techniques to enable ldquosocial searchrdquo based on real-time user interests and finds potential matches for the proposed activity. Further, it exploits next-generation presence and communication technologies to manage the entire activity lifecycle in real-time. Initial survey results, based on a prototype implementation of R-U-In?, attest to the promise of realtime activity-oriented social networking - both in terms of an effective collaboration tool for value-oriented social networking users and an enhanced end-user experience.

Presence, broadly defined as an event publish-notification infrastructure for converged applications, has emerged as a key mechanism for collecting and disseminating context attributes for next-generation services in both enterprise and provider domains. Current presence-based solutions and products lack in the ability to a) support flexible user-defined queries over dynamic presence data and b) derive composite presence from multiple provider domains. Accordingly, current uses of context are limited to individual domains/organizations and do not provide a programmable mechanism for rapid creation of context-aware services. This paper describes a presence virtualization architecture, where a Virtualized Presence Server receives customizable queries from multiple presence clients, retrieves the necessary data from the base presence servers, applies the required virtualization logic and notifies the presence clients. To support both query expressiveness and computational efficiency, virtualization queries are structured to separately identify both the XSLT-based transformation primitives and the presence sources over which the transformation occurs. For improved scalability, the proposed architecture offloads the XSLT-related processing to a high-performance XML processing engine. We describe our current implementation and present performance results that attest to the promise of this virtualization approach.

Social Network Analysis has emerged as a key paradigm in modern sociology, technology, and information sciences. The paradigm stems from the view that the attributes of an individual in a network are less important than their ties (relationships) with other individuals in the network. Exploring the nature and strength of these ties can help understand the structure and dynamics of social networks and explain real-world phenomena, ranging from organizational efficiency to the spread of information and disease.

This paper presents R-U-In? - a social networking application that leverages Web 2.0 and IMS-based Converged Networks technologies to create a rich next-generation service. R-U-In? allows a user to search (in real-time) and solicit participation of like-minded partners for an activity of mutual interest (e.g. a rock concert, a soccer game, or a movie). It is an example of a situational mashup application that exploits content and capabilities of a Telecom operator, blended with Web 2.0 technologies, to provide an enhanced, value-added service experience.

Various sectors in developing countries are typically dominated by the presence of a large number of small and microbusinesses that operate in an informal, unorganized manner. Many of these are single person run micro-businesses and cannot afford to buy and maintain their own IT infrastructure. For others, easy availability of cheap labour provides a convenient alternative even though it results in inefficiency, as little or no records are maintained, and only manual, paper-based processes are followed. This results in high response times for customers, no formal accountability and higher charges. For the businesses this translates to lower earnings and losses due to inefficiencies. In this paper, we look at few such micro-business segments and explore their current models of operation, while identifying existing inefficiencies and pain points. We build upon the findings and propose an approach for delivering benefits of IT solutions to such micro-business segments. Finally, we present technology that realizes the proposed approach in the specific context of two such segments.

With Telecom market reaching saturation in many geographies and revenues from voice calls decreasing, Telecom operators are trying to identify new sources of revenue. For this purpose, these operators can take advantage of their core functionalities like Location, Call Control, etc. by exposing them as services to be composed by developers with third party offerings available over the Web. To hide the complexity of underlying Telecom protocols from application developers, the operators are steadily adopting Service Oriented Architecture (SOA) and reference standards like Parlay-X and IMS. However, a number of challenges still remain in rapid utilization of Telecom functionalities for creating new applications - existence of multiple protocols, different classes of developers, and the need to coordinate and manage usage of these functionalities. In this paper, we present SewNet, a framework for creating applications exploiting Telecom functionality exposed over a (converged) IP network. More specifically, SewNet a) provides an abstraction model for encapsulating invocation, coordination and enrichment of the Telecom functionalities, b) renders a service creation environment on top of this model, and c) caters to various different categories of developers. With the help of two use-case scenarios, we demonstrate how SewNet can create services utilizing rich Telecom functionality.

With the ever-growing competition in telecommunications markets, operators have to increasingly rely on business intelligence to offer the right incentives to their customers. Existing approaches for telecom business intelligence have almost solely focused on the individual behavior of customers. In this paper, we use the call detail records of a mobile operator to construct call graphs, that is, graphs induced by people calling each other. We determine the structural properties of these graphs and also introduce the Treasure-Hunt model to describe the shape of mobile call graphs. Moreover, we determine how the structure of these call graphs evolve over time. Finally, since short messaging service (SMS) is becoming a preferred mode of communication among many sections of the society, we study the properties of the SMS graph. Our analysis indicates several interesting similarities and differences between the SMS graph and the corresponding call graph. We believe that our analysis techniques can allow telecom operators to better understand the social behavior of their customers and potentially provide major insights for designing effective incentives.

This paper presents the design and implementation of Heterogeneous Event Management Middleware (HEMM) that integrates events across multiple siloed workflow management systems (WFMS) in enterprises. Specifically, we address the issue where a high-level change (e.g. change in business policy or rolling out of a new service) necessitates external events (generated from other enterprise system) to be handled by running workflow (WF) instances in the WFMS. Current solutions either require a change in the workflow process definition or change in the workflow engine - both being cost intensive solutions from an enterprise 's point of view since they compromise on enterprise backend downtime. HEMM circumvents this by introducing an overlay on top of existing WFMS and abstracting out unforeseen event handling from workflows. At the same time, it employs event transformation to map events to appropriate event handlers of running workflow instances, enabling them to adapt to new events that their process definitions do not handle. We demonstrate our prototype implementation with the example of rolling out a bundled service that is prevalent in telecom industry.

WWW has made information accessible to computer users in various ways not imagined before. However; there is a huge pool ofpeople, especially in emerging economies, still untouched by this revolution and are either unaware of, or are unable or to join this bandwagon. Mobile phones are increasingly empowering the under-privileged to utilize data and services beyond the basic voice communication. However; factors such as high illiteracy rate, cost sensitivity, and user interface issues prevent these users from deriving benefits of available infrastructure and services. We have developed a novel system - VOISERV that enables ordinary telephone subscribers to create, deploy and offer their own customized voice-driven applications called Voic-eSites. The generated VoiceSites get hosted in the network for low cost of ownership and maintenance, and are integrated with advanced services available in the converged networks of today.

With ever growing competition in telecommunications markets, operators have to increasingly rely on business intelligence to offer the right incentives to their customers. Toward this end, existing approaches have almost solely focussed on the individual behaviour of customers. Call graphs, that is, graphs induced by people calling each other, can allow telecom operators to better understand the interaction behaviour of their customers, and potentially provide major insights for designing effective incentives.In this paper, we use the Call Detail Records of a mobile operator from four geographically disparate regions to construct call graphs, and analyse their structural properties. Our findings provide business insights and help devise strategies for Mobile Telecom operators. Another goal of this paper is to identify the shape of such graphs. In order to do so, we extend the well-known reachability analysis approach with some of our own techniques to reveal the shape of such massive graphs. Based on our analysis, we introduce the Treasure-Hunt model to describe the shape of mobile call graphs. The proposed techniques are general enough for analysing any large graph. Finally, how well the proposed model captures the shape of other mobile call graphs needs to be the subject of future studies.

A telecom operator ("service provider",SP) offers various services to subscribed customers by partnering with various third party providers ("content provider",CP). The SP acts as a liaison between subscribers and partners. One of the main functions of the SP, therefore, is to match the "demand" of the subscribers with the "supply" of the CPs. Such a matching is a prerequisite for efficient service selection while ensuring customer satisfaction, and is useful for optimisation, such as resource allocation and load balancing. The "demand" requirements and "supply" guarantees can be concretized using Service Level Agreements (SLAs). SLAs can be expressed formally using standards such as WSLA or WS-Agreement. We present a system that automates the task of finding a matching between these two sets, subscriber-SP and SP-CP, of SLAs. First, the SLAs are normalised to a common denominator, then composed if required, and finally the matching engine computes and outputs the map. The matching algorithm, which is of central importance, compares logical expressions involving predicates. The logical expressions are first converted into CNFform, and instead of naive O(m × n) comparisons, we develop a more efficient approach to solve the problem. As a proof-of-concept, we have implemented a prototype as an Eclipse plugin.

Next-generation converged networks shall deliver many innovative services over the standardized SIPbased IMS signaling infrastructure. Several such services exploit the joint presence information of a consumer, i.e. SIP entity requesting a service, and a vendor, i.e. SIP resource providing a service. Presence information is a collection of contextual attributes (e.g. location, availability, reputation), some of which change dynamically. Moreover, this collective presence information is distributed across multiple presence servers. While performing query matching based on joint presence information, a server usually routes each query to a locally available resource. However, skews in the spatio-temporal distribution of queries and resources may require queries to be routed to alternate servers with available resources. We propose a novel Resource-Aware Query Routing scheme, called RAQR, where each server proactively establishes gradients to suitable servers via a diffusion-based algorithm. Gradients are set up whenever a server anticipates scarcity of resources and withdrawn when the resource crunch is mitigated. We compare RAQR with alternative resource matching schemes and show that it adapts to spatio-temporal variations in resource availability, thereby leading to effective query matching with minimal control overhead.

The paper proposes a novel distributed service discovery protocol for pervasive environments. The protocol is based on the concepts of peer-to-peer caching of service advertisements and group-based intelligent forwarding of service requests. It does not require a service to be registered with a registry or lookup server. Services are described using the Web Ontology Language (OWL). We exploit the semantic class/subClass hierarchy of OWL to describe service groups and use this semantic information to selectively forward service requests. OWL-based service description also enables increased flexibility in service matching. We present simulation results that show that our protocol achieves increased efficiency in discovering services (compared to traditional broadcast-based mechanisms) by efficiently utilizing bandwidth via controlled forwarding of service requests.

This paper presents an architecture to facilitate efficient evaluation and selection of 3rd party Web services for service providers. Most service provider architectures have primarily focused on providing Web service front ends to legacy systems, aggregating and delivering services via workflows. These architectures primarily considered static business contracts between the service provider and its (Web-service enabled) business partners. This approach makes these architectures inflexible to variations in business requirement, partners' performance and customer requirements. Our architecture provides a flexible means for service providers to optimize business performance. Based on the historical performance, extant context, and optimising business rules, the appropriate service is selected and invoked to serve a customer request. We have developed a prototype system ODSS-in-ENDS, that demonstrates this capability.

The provisioning of value-added-services (VAS) to mobile users is crucial for the proliferation and early adoption of converged voice and data networks, 3rdgeneration/beyond 3rdgeneration networks (3G/B3G). In this paper, we describe a VAS provisioning middleware framework for deployment in 3G mobile operator networks. Through this framework, we enable on-demand selection of 3rdparty services in 3G/B3G networks. The main contribution, in terms of novelty, is that the framework introduces a new service provisioning paradigm called on demand service selection. The service selection (a requested user task can be accomplished by many similar service offerings, therefore the need for selecting a particular service) is driven by the 3G operator business policies, user profile/preferences and runtime context information. Also, the framework adapts the service selection to changing business-technical performances of the 3rd party CP/SP, and specific user preferences and context

People are an important part of many business processes. Current workflow-based implementations of business processes constrain users to the desktop environment; require them to periodically check for pending tasks; and do not support direct or synchronous people-to-people interaction. On the other hand, the wide spectrum of people collaboration tools ranging from telephones to instant messaging and to email have no provision for structured collaboration and are separate from business processes. We have designed and implemented a system, dubbed PerCollab that integrates workflow and collaboration technologies. It allows people to participate in business processes from anywhere using a traditional collaboration mechanism. It proactively engages users in business processes by pushing interaction to a convenient device of the users. PerCollab uses an extended BPEL to formally define business processes with human partners and exploits dynamic user context to solve the personal mobility problem. Our prototype implementation integrates a variety of collaboration tools: instant messaging, email and e-meeting.

Service composition in pervasive environments enables users to utilize services in the environment to solve complex queries. Current work in development of service composition architectures focuses on wired-networked environments where solutions are centralized and tailored towards a reliable network and fixed service topology. In this paper, we present an alternate and novel design architecture of a broker-based distributed service composition protocol for pervasive environments. We present simulation results by comparing our protocol to a centralized architecture for composition. Results show that our distributed broker-based composition architecture perform better than the centralized solution in terms of composition efficiency, broker arbitration efficiency and composition radius.