Table of Contents
Introduction
3
Role of Mobile devices as “Rich Sensors”
3
Emergent Theme of “Near-Real Time Data Consistency”
5
Mobile Opportunism
7
Certain Fundamental Challenges of Mobility
9
Conclusion
10
Bibliography
11
Telecommunication world is an environment that has seen magnificent changes especially over the last two decades. The changes experienced are more pronounced in mobile communication systems and improvement of related technologies. In this report, the learner takes a closer look at mobile devices, merging themes in line with mobility and identification of mobile opportunism, in addition to, underlying challenges, in mobility.
Role of Mobile devices as “Rich Sensors”
The emergence of mobile devices was such that these devices would enable communication between two or more parties from virtually any point. The transition from wired to wireless communication system has remained to be a great stride in telecommunication (see Shah and Akan 2010, Forouzan 2007, Teece 2010). For this reason, the platform created has enabled faster redesign and development of configuration systems that can be used as remote sensors. For example, according to Shah and Akan (2010), on “wireless sensor actor network (WSAN), it is noted that this is a relatively new class in computing, which consists of a large number of sensor nodes, in addition to, relatively smaller number of actor nodes” (p. 664). These actor nodes and sensor nodes are embedded within the same operating environment (Shah and Akan 2010). The splendor of this environment is the fact that these nodes are spatially distributed over vast geographical area covering remote and grossly inaccessible localities (Shah and Akan 2010).
On one end, sensor nodes are tiny in nature, resource-constrained devices that have the capability to sense, communicating and performing computation (Shah and Akan 2010). On the other end, “actors are resource-rich, mobile devices that have the capability to make decisions autonomously, execute actions depending on the inputs received from sensor nodes” (Shah and Akan 2010, p. 664). This autonomy in decision-making makes it easier to use these actor nodes to “instrument, observe and respond to the physical world” on a timescale that is initially thought to be unachievable (Shah and Akan 2010, p. 664).
A fact about sensors is that they are closely coupled with the physical environment and thus it is imperative they are made aware of their geographical locus such that they represent the area under observation (Shah and Akan 2010). This information on geographical position is necessary to actors (mobile devices) such that they can actively “monitor and respond to the region of interest” (Shah and Akan 2010, p. 665). Take for example, when monitoring seismic activities especially in areas prone to volcanic eruptions like Ecuador, it is imperative to have tools that can measure and send a signal and data in near-real time consistency (Shah and Akan 2010) and (Zoumboulakis and Roussos 2011).
Other areas of applications range from healthcare facilities that exchange data about patients remotely located and need immediate medical attention to military personnel in remote areas needing tactical support and strategies (Shah and Akan 2010). For an actor to synthesize information input, identification of signal origin is a prerequisite so that the actor can effectively act upon the event (Shah and Akan 2010).
Zoumboulakis and Roussos (2011) identify application of mobile devices as rich-sensors in chemical dispersion where levels and types of chemical concentration are measured, quantified and further analysis done from remote areas. The dispersion patterns plotted can then be used to determine various approaches, for example, in an event of chemical or biological warfare, how to deal/ evacuate people lying within danger paths. Zoumboulakis and Roussos (2011) provide scientists with the ability to “search and identify such difficult patterns, which using conventional methods, would be an impossible task to capture such data” (p. 211).
Emergent Theme of “Near-Real Time Data Consistency”
Recent advancement in technology has shown that there is an increasing focus on near-real time advancement of technology (Viana et al. 2011). Application of near-real time data consistency has been driven by the fact that increasing use of multimedia documents especially in mobile devices has necessitated advancement of this technology (Viana et al. 2011). It is noted that modern mobile device users nowadays can create, with much ease, “large quantities of multimedia documents, which entail capturing significant events that they attend, moments of their daily lives or capturing activities of places that they visit” (p. 391). Additionally, with the development of mobile devices that can interactively play online games, movies and other real-time mannered activities, the need for this advancement has increased especially for device manufacturers. Activities and video clips in the modern world are then shared online via sites like “Yahoo!, TripperMap, YouTube, Flickr and DailyMotion to mention but a few” (Viana et al. 2011, p. 392).
Retrieval of the shared multimedia demands near-real time buffering so that the viewer does not get bored while waiting for the clip to buffer over an extended length of time (Viana et al. 2011). Looking at this emergent theme from another viewpoint, while dealing with weather and hydrological evaluations, near-real time data collection and analysis has become vital (Yang et al. 2010). For example, in areas, which are prone to heavy precipitation, cyclones, tornados, prolonged, and catastrophic heat waves and earthquakes, near-real time measurement of these events as they unfold has proved vital to salvage as many lives as possible (Yang et al. 2010). This therefore demands that environmental monitoring systems have overall architecture that safeguards “seamless integration for wired, as well as wireless sensors especially on a long-term basis” (Yang et al. 2010, p. 1091).
Yang et al. (2010) continues to posit that many scientific deployments in the past focused their experimentations and deployments based on controlled environment instead of having these deployments in near-real life application environment. However, this trend has gradually, yet consistently, changed in recent times with ecological and environmental scientists leading the way (Yang et al. 2010). The development of cyber infrastructure aimed at relaying environmental and observatory data in near-real time has become a reality highly attributed to utilization of a variety of sensor systems, in addition to, sophisticated computational resources and informatics (Yang et al. 2010). The information “observed, help in modeling, predicting and ultimately aid in preservation of a healthy natural environment” (Yang et al. 2010, p. 1091).
Dlamini (2011), while discussing applications of near-real time technology notes that fire risk mapping has become an essential aspect especially in regions where fire instances is prone. Fire risk mapping is made possible by utilization of geographic information systems (GIS), in addition to, remote sensing data collecting technologies available in the market (Dlamini 2011). The necessity for near-real time data collection that is exemplified in the works of Yang et al. (2010) becomes an essential feature in the rescue and evacuation operations since such information collection, analysis and relay needs to be time sensitive. Using Earth Observatory (EO) satellites, it is possible to capture synoptic snapshots as regards to fire activity in near-real time that extends in operational fire management (Dlamini 2011). The data collected can then be analyzed using near-real time fire risk analysis techniques that give informational support and better decision-making support framework in near-real time to fire fighters on the ground (Dlamini 2011). In some instances, utility of multi-satellite analysis is vital for data synthesis and relay of this information in near-real time if these satellites have the correct configuration for such complex analysis and decision-making intelligence.
Mobile Opportunism
An adage posits that it is the sole business of any enterprise to make a profit. With this fact in mind, it is clear that telecommunication enterprises have an inherent mandate to make as much profit as possible. Companies and thus increasing profitability have over time used this notion to make radical decisions that curtail extravagant expenditure on various fronts. According to Vandenbosch and Sapp (2010), for any company to compete effectively and thrive in a volatile environment, it is imperative for such companies to respond actively to consumer demands. Consumers, in addition to product and/ or service quality, are relatively sensitive to the price of these products and/ or services offered by the company (Vandenbosch and Sapp 2010). This drive toward price sensitivity is attributed to the fact that a combination of globalization force and information technology enhancement has precipitated a paradigm shift in competitive milieu on diverse industries (Vandenbosch and Sapp 2010).
Globalization has enabled outsourcing of manufacturing and other services such that inherent costs that initially were covered by full-time employees are abridged and thus a reduction in the overall cost of production and design (Vandenbosch and Sapp 2010). This, therefore, provides firms with opportunities to produce products and services at a relatively cheaper stance. Companies have relentlessly embarked on price-cutting strategy to gain as much market share as possible, to the advantage of consumers who have to spend less to achieve the same goal that initially was premium priced. In addition to outsourcing non-core activities, some firms have gone a stride further to clasp specialization, an aspect that has proved to offers some companies an edge over their rivals (Vandenbosch and Sapp 2010).
However, despite such high astute stance adopted, it is necessary to note that the current trend has seen locational optimization of services instead of system-wide optimization. This has led to some parts having sophisticated systems while, in other regions, the same sophistication is lacking despite both consumers sharing similar products and/ or services (Vandenbosch and Sapp 2010). For example, in a demand-supply scenario, it is essential to note, “The longer the supply chain, the higher is the risk of the emergence of opportunism” (Vandenbosch and Sapp 2010, p. 18). Another identified issue is that in most cases, when one is protected against a threat, there is an inherent lax by such a person to take an active stance to guard themselves against such risks, which communally are termed as moral hazards, an aspect that is prone to most mobile users (Vandenbosch and Sapp 2010).
Teece (2010) adds to the notion stating that current mobile trends have centralized towards forward integrations, in addition to, innovations that reduce transaction costs both in the immediate status and future. This is made possible by the fact that firms are forming coalitions and alliances such that there is sharing of technology to provide a common platform for individual consumers (Forouzan 2007).
In summary, mobile opportunism is the identification of an opportunity in the mobile market, and orienting the company towards this direction such that there is optimization inclined towards that direction for faster market penetration. However, the downside of this approach is the fact that there is a lot of negligence in as far as consideration of fundamentally crucial aspects is concerned.
Certain Fundamental Challenges of Mobility
Mobility has over time, had some inherent challenges that have marred the industry since its coming into existence. In the works of Satyanarayanan (1999), there are four identified challenges that never seem to wane away despite all efforts to deal with the problems. Firstly, it is noted that mobile elements are inherently resource-poor as it relates to static elements (Satyanarayanan 1999) and (Forouzan 2007). For every consideration of cost and the level of technological advancement, there is always additional consideration of weight, size, power and ergonomics, which tends to exert penalties on the side of computational resource (Satyanarayanan 1999). A good example towards this includes penalties on processor speed, disk capacity and memory size, aspects supported through various policies established by various bodies of regulators (Satyanarayanan 1999). While these mobile devices will have improvements in their absolute abilities, it is also sad to note that these mobile devices will always be resource-poor as it relates to static elements (Satyanarayanan 1999).
Secondly, mobility in itself is naturally harmful (Satyanarayanan 1999). Satyanarayanan (1999) gives an amusing example where it is identified that a Wall Street stockbroker is highly likely to be mugged and have the laptop stolen than the same happening on his workstation. Personal computers remaining relatively immobile or locked within the premise are less likely to be stolen unlike mobile devices that carry sensitive information that keep moving from place to place. Their ease of portability increases their risk of getting lost. Additionally, these devices are also prone to loss or damages upon hitting the ground since they are in motion and thus their risk is higher as compared to static devices (Satyanarayanan 1999).
Thirdly, mobile connectivity tends to be highly variable in as far as performance and reliability is concerned (Satyanarayanan 1999). For example, in some localities, connectivity may be abundant with high bandwidth rates while, in others, the reverse may be applicable with relatively low or no connectivity and miniscule bandwidth available (Satyanarayanan 1999). While on transit, mobile users have to rely on low-bandwidth wireless networks, which offer some gaps in as far as coverage is concerned (Satyanarayanan 1999).
Lastly, mobile elements rely heavily on finite sources of energy (Satyanarayanan 1999). Although battery technology has undoubtedly improved and continues to improve over time, the necessity for consistent power sensitivity will not abate (Satyanarayanan 1999). For this reason, power consumption concerns have to be considered through the eyes of hardware and software levels so that there is effective power utility (Satyanarayanan 1999).
Conclusion
In conclusion, mobile devices and mobile technology has over time found much application. Most of the application in modern day demands near-real time data collection and synthesis for efficient decision-making, and decision support systems especially when dealing with time-sensitive phenomena. Mobile devices have turned to be rich sensors where they collect, analyses and transmit data to various locales while also having capabilities of activating actions remotely. Companies have in recent times embarked on mobile opportunism where there is specialization and optimization for cost effectiveness and profitability. Lastly, the ubiquitous challenges that are inherent to all mobile devices remains a menace whose solution is yet to be found despite improvement in mobile technology since inception.
Bibliography
Dlamini, W.M. 2011, "Application of Bayesian networks for fire risk mapping using GIS and remote sensing data", Geo Journal, vol. 76, no. 3, pp. 283-296.
Forouzan, B. A. 2007, Data Communications and Networking, 4 ed., McGraw-Hill Companies, Inc.: San Francisco. ISBN: 9780073250328
Teece, D.J. 2010, "Forward Integration and Innovation: Transaction Costs and Beyond", Journal of Retailing, vol. 86, no. 3, pp. 277-283.
Satyanarayanan, M. 1999, Fundamental Challenges in Mobile Computing, School of Computer Science: Carnegie Mellon University.
Shah, G.A. & Akan, O.B. 2010, "Timing-Based Mobile Sensor Localization in Wireless Sensor and Actor Networks", Mobile Networks and Applications, vol. 15, no. 5, pp. 664-679.
Vandenbosch, M. & Sapp, S. 2010, "Opportunism Knocks", MIT Sloan Management Review, vol. 52, no. 1, pp. 17-19.
Viana, W., Miron, A.D., Moisuc, B., Gensel, J., Villanova-Oliver, M. & Martin, H. 2011, "Towards the semantic and context-aware management of mobile multimedia", Multimedia Tools and Applications, vol. 53, no. 2, pp. 391-429.
Yang, J., Zhang, C., Li, X., Huang, Y., Fu, S. & Acevedo, M.F. 2010, "Integration of wireless sensor networks in environmental monitoring cyber infrastructure", Wireless Networks, vol. 16, no. 4, pp. 1091-1108.
Zoumboulakis, M. & Roussos, G. 2011, "Complex Event Detection in Extremely Resource-Constrained Wireless Sensor Networks", Mobile Networks and Applications, vol. 16, no. 2, pp. 194-213.
